--------------------------------------------------------------------------------------------------
      name:  logfile
       log:  C:\Users\felix\Desktop\replicationfiles\logfile.log
  log type:  text
 opened on:   4 Dec 2022, 17:07:52

. do "code/1dataprep.do"

. clear

. 
. ***EU
. foreach x in BE CH DE DK NL SE UK NO IT FI{
  2. import excel "data/EU/`x'full4.xlsx", firstrow clear
  3. gen country="`x'"
  4. tostring D3other, replace
  5. tostring M2other, replace
  6. tostring comments, replace
  7. save "data/`x'full4.dta", replace
  8. }
D3other was byte now str1
M2other already string; no replace
comments already string; no replace
file data/BEfull4.dta saved
D3other already string; no replace
M2other already string; no replace
comments already string; no replace
file data/CHfull4.dta saved
D3other already string; no replace
M2other already string; no replace
comments already string; no replace
file data/DEfull4.dta saved
D3other already string; no replace
M2other already string; no replace
comments already string; no replace
file data/DKfull4.dta saved
D3other already string; no replace
M2other already string; no replace
comments already string; no replace
file data/NLfull4.dta saved
D3other already string; no replace
M2other already string; no replace
comments already string; no replace
file data/SEfull4.dta saved
D3other already string; no replace
M2other already string; no replace
comments already string; no replace
file data/UKfull4.dta saved
D3other was byte now str1
M2other already string; no replace
comments already string; no replace
file data/NOfull4.dta saved
D3other already string; no replace
M2other already string; no replace
comments already string; no replace
file data/ITfull4.dta saved
D3other was byte now str1
M2other was byte now str1
comments was byte now str1
file data/FIfull4.dta saved

. 
. use "data/BEfull4.dta", clear

. append using "data/CHfull4.dta" "data/DEfull4.dta" "data/DKfull4.dta" "data/NLfull4.dta" "data/S
> Efull4.dta" "data/UKfull4.dta" "data/NOfull4.dta" "data/ITfull4.dta" "data/FIfull4.dta"
(note: variable id was byte, now int to accommodate using data's values)
(note: variable vig1out1 was str370, now str459 to accommodate using data's values)
(note: variable vig4out1 was str132, now str318 to accommodate using data's values)
(note: variable M1 was str42, now str184 to accommodate using data's values)
(note: variable M2other was str30, now str47 to accommodate using data's values)
(note: variable D1 was str6, now str20 to accommodate using data's values)
(note: variable D3other was str1, now str42 to accommodate using data's values)
(note: variable Email was str29, now str34 to accommodate using data's values)
(note: variable comments was str180, now str971 to accommodate using data's values)
(note: variable vig1out1 was str459, now str894 to accommodate using data's values)
(note: variable vig3out1 was str288, now str926 to accommodate using data's values)
(note: variable vig4out1 was str318, now str624 to accommodate using data's values)
(note: variable M1 was str184, now str262 to accommodate using data's values)
(note: variable M2other was str47, now str88 to accommodate using data's values)
(note: variable D2 was byte, now double to accommodate using data's values)
(note: variable Email was str34, now str104 to accommodate using data's values)
(note: variable attentionSQ006 was str2, now str3 to accommodate using data's values)
(note: variable M2other was str88, now str128 to accommodate using data's values)
(note: variable D3other was str42, now str71 to accommodate using data's values)
(note: variable attentionSQ005 was str2, now str3 to accommodate using data's values)
(note: variable M1 was str262, now str405 to accommodate using data's values)
(note: variable M2other was str128, now str337 to accommodate using data's values)
(note: variable D3 was str19, now str21 to accommodate using data's values)
(note: variable comments was str971, now str1483 to accommodate using data's values)

. save "data/EUfull4.dta", replace
file data/EUfull4.dta saved

. 
. drop dataprot* *Time* M3* submitdate startlanguage sec* Eligibility vig1out1 vig3out1 vig4out1 E
> mail comments vig2a vig2b vig1a vig1b vig1c vig1d vig1e vig1f vig1g vig1h vig1out2 vig1aV2 vig1b
> V2 vig1cV2 vig1dV2 vig1eV2 vig1fV2 vig1gV2 vig1hV2 vig1out1V2 vig3a vig3b vig3out2SQ001 vig3out2
> SQ003 vig3out2SQ002 vig3aV2 vig3bV2 vig3out1V2 vig4a vig4b vig4out2SQ001 vig4out2SQ002 vig4aV2 v
> ig4bV2 vig4out1V2

. 
. **rename variables
. rename D1 sex

. rename D2 age

. rename D3 status

. rename D3other statusother

. rename attentionSQ001 attention1

. rename attentionSQ002 attention2

. rename attentionSQ003 attention3

. rename attentionSQ005 attention4

. rename attentionSQ006 attention5

. rename M1 numberofcases

. rename M2SQ001 stats     

. rename M2SQ002 QCA

. rename M2SQ003 process

. rename M2SQ004 interpret

. rename M2SQ005 experiment

. rename M2other othermethod

. 
. *recode age
. gen age2= age
(400 missing values generated)

. replace age2=. if age2>100 
(4 real changes made, 4 to missing)

. replace age2=. if age2<18 
(3 real changes made, 3 to missing)

. egen agemean = mean(age2)

. replace age2 = agemean if age2==. & age !=.
(7 real changes made)

. drop age

. rename age2 age

. label variable age "age"

. 
. *label randomized treatments
. label define randvig1 1 "small N, binary FI, binary SS" 2 "large N, binary FI, binary SS" 3 "sma
> ll N, binary FI, continuous SS" 4 "large N, binary FI, continuous SS" 5 "small N, continuous FI,
>  binary SS" 6 "large N, continuous FI, binary SS" 7 "small N, continuous FI, continuous SS " 8 "
> large N, continuous FI, continuous SS"

. label values rand1 randvig1

. label define randvig2 1 "extensively studied" 2 "not extensively studied"

. label values rand2 randvig2

. label define randvig3 1 "small N" 2 "large N"

. label values rand3 randvig3

. label define randvig4 1 "small N" 2 "large N"

. label values rand4 randvig4

. 
. *label manipulation checks
. label variable Manipulate1 "Y-centric"

. label variable Manipulate2 "large N"

. label variable Manipulate3 "measurement level"

. 
. *label attention checks
. label variable attention1 "Globalization and welfare state"

. label variable attention2 "Regime form and interstate war"

. label variable attention3 "Economic development and democracy"

. label variable attention4 "Civil society and elections"

. label variable attention5 "Climate change and economic development"

. 
. *label culture variables
. label variable numberofcases "number of cases"

. label variable stats "Statistical analysis"

. label variable QCA "QCA"

. label variable process "Process Tracing"

. label variable interpret "Interpretivist qualitative methods"

. label variable experiment "Experiments"

. label variable othermeth "Other"

. 
. *relabel outcome variables
. ***Vignette1
. *vig1opt1: "If foreign investment in a country changes from low to high, it gets more likely tha
> t social spending is high"
. gen vig1opt1=.
(906 missing values generated)

. replace vig1opt1=1 if vig1out2V2SQ002=="Yes"
(88 real changes made)

. replace vig1opt1=0 if lastpage>4 & vig1opt1==.
(493 real changes made)

. 
. *vig1opt2: "If foreign investment in a country is high, then social spending is high"
. gen vig1opt2=.
(906 missing values generated)

. replace vig1opt2=1 if vig1out2V2SQ003=="Yes"
(260 real changes made)

. replace vig1opt2=0 if lastpage>4 & vig1opt2==.
(322 real changes made)

. 
. *vig1opt3: "If foreign investment in a country changes from low to high, it gets more likely tha
> t social spending increases"
. gen vig1opt3=.
(906 missing values generated)

. replace vig1opt3=1 if vig1out2V2SQ004=="Yes"
(100 real changes made)

. replace vig1opt3=0 if lastpage>4 & vig1opt3==.
(482 real changes made)

. 
. *vig1opt4: "As foreign investment increases, it gets more likely that social spending changes fr
> om low to high and as foreign investment decreases, it gets more likely that social spending cha
> nges from high to low"
. gen vig1opt4=.
(906 missing values generated)

. replace vig1opt4=1 if vig1out2V2SQ005=="Yes"
(119 real changes made)

. replace vig1opt4=0 if lastpage>4 & vig1opt4==.
(463 real changes made)

. 
. *vig1opt5: "If foreign investment in a country is high, then social spending is high and if fore
> ign investment in a country is low, then social spending is low"
. gen vig1opt5=.
(906 missing values generated)

. replace vig1opt5=1 if vig1out2V2SQ006=="Yes"
(320 real changes made)

. replace vig1opt5=0 if lastpage>4 & vig1opt5==.
(262 real changes made)

. 
. *vig1opt6: "As foreign investment increases, it gets more likely that social spending increases 
> and as foreign investment decreases, it gets more likely that social spending decreases"
. gen vig1opt6=.
(906 missing values generated)

. replace vig1opt6=1 if vig1out2V2SQ007=="Yes"
(165 real changes made)

. replace vig1opt6=0 if lastpage>4 & vig1opt6==.
(417 real changes made)

. 
. *vig1opt7: "If foreign investment in a country is low, then social spending is low"
. gen vig1opt7=.
(906 missing values generated)

. replace vig1opt7=1 if vig1out2V2SQ008=="Yes"
(254 real changes made)

. replace vig1opt7=0 if lastpage>4 & vig1opt7==.
(328 real changes made)

. drop vig1out2V2SQ*

. 
. **Vignette2
. replace vig2out2="A1" if vig2out2=="You conduct an exploratory analysis to identify the causes o
> f the phenomenon"
(363 real changes made)

. replace vig2out2="A2" if vig2out2=="You conduct a confirmatory study to evaluate how particular 
> factors on average influence the phenomenon"
(159 real changes made)

. replace vig2out2="A3" if vig2out2=="You conduct a confirmatory study that traces the process of 
> how particular factors influence the phenomenon within individual cases"
(232 real changes made)

. gen vig2outcome=.
(906 missing values generated)

. replace vig2outcome=1 if vig2out2=="A1" 
(363 real changes made)

. replace vig2outcome=2 if vig2out2=="A2" 
(159 real changes made)

. replace vig2outcome=3 if vig2out2=="A3" 
(232 real changes made)

. label define vig2outcome 1 "exploratory" 2 "confirmatory" 3 "process"

. label values vig2outcome vig2outcome

. drop vig2out2

. 
. **Vignette3
. *vig3opt1: "Democratic dyad is a sufficient condition for interstate peace"
. gen vig3opt1=.
(906 missing values generated)

. replace vig3opt1=1 if vig3out2V2SQ001=="Yes"
(212 real changes made)

. replace vig3opt1=0 if vig3out2V2SQ001=="No"
(307 real changes made)

. replace vig3opt1=. if vig3out2V2SQ001=="N/A"
(0 real changes made)

. *vig3opt2: "Nondemocratic dyad is a necessary condition for war between states"
. gen vig3opt2=.
(906 missing values generated)

. replace vig3opt2=1 if vig3out2V2SQ002=="Yes"
(215 real changes made)

. replace vig3opt2=0 if vig3out2V2SQ002=="No"
(304 real changes made)

. replace vig3opt2=. if vig3out2V2SQ002=="N/A"
(0 real changes made)

. *vig3opt3: "If two states are democratic, interstate war between them is less likely"
. gen vig3opt3=.
(906 missing values generated)

. replace vig3opt3=1 if vig3out2V2SQ003=="Yes"
(362 real changes made)

. replace vig3opt3=0 if vig3out2V2SQ003=="No"
(157 real changes made)

. replace vig3opt3=. if vig3out2V2SQ003=="N/A"
(0 real changes made)

. drop vig3out2V2SQ*

. 
. **Vignette4
. *vig4opt1: "Economic development is necessary to achieve democracy"
. gen vig4opt1=.
(906 missing values generated)

. replace vig4opt1=1 if vig4out2V2SQ001=="Yes"
(25 real changes made)

. replace vig4opt1=0 if vig4out2V2SQ001=="No"
(483 real changes made)

. replace vig4opt1=. if vig4out2V2SQ001=="N/A"
(0 real changes made)

. *vig4opt2: "The level of economic development is positively related to the level of democracy"
. gen vig4opt2=.
(906 missing values generated)

. replace vig4opt2=1 if vig4out2V2SQ002=="Yes"
(499 real changes made)

. replace vig4opt2=0 if vig4out2V2SQ002=="No"
(9 real changes made)

. replace vig4opt2=. if vig4out2V2SQ002=="N/A"
(0 real changes made)

. drop vig4out2V2SQ*

. 
. save "data/EUfull4.dta", replace
file data/EUfull4.dta saved

. 
. **USA
. import excel "data/USA/USfull5.xlsx", firstrow clear

. gen country="USA"

. drop *Time* M3* submitdate startlanguage sec* Eligibility vig1out1 vig3out1 vig4out1 Email comme
> nts vig2a vig2b vig1a vig1b vig1c vig1d vig1e vig1f vig1g vig1h vig1out2 vig1aV2 vig1bV2 vig1cV2
>  vig1dV2 vig1eV2 vig1fV2 vig1gV2 vig1hV2 vig1out1V2 vig3a vig3b vig3out2SQ001 vig3out2SQ003 vig3
> out2SQ002 vig3aV2 vig3bV2 vig3out1V2 vig4a vig4b vig4out2SQ001 vig4out2SQ002 vig4aV2 vig4bV2 vig
> 4out1V2

. 
. **rename variables
. rename D1 sex

. rename D2 age

. rename D3 status

. rename D3other statusother

. rename attentionSQ001 attention1

. rename attentionSQ002 attention2

. rename attentionSQ003 attention3

. rename attentionSQ005 attention4

. rename attentionSQ006 attention5

. rename M1 numberofcases

. rename M2SQ001 stats     

. rename M2SQ002 QCA

. rename M2SQ003 process

. rename M2SQ004 interpret

. rename M2SQ005 experiment

. rename M2other othermethod

. 
. *recode age
. destring age, gen(age2) force
age: all characters numeric; age2 generated as double
(265 missing values generated)

. replace age2=. if age2>100 
(5 real changes made, 5 to missing)

. replace age2=. if age2<18 
(3 real changes made, 3 to missing)

. egen agemean = mean(age2)

. replace age2 = agemean if age2==. & age !=""
(8 real changes made)

. drop age

. rename age2 age

. label variable age "age"

. 
. *label randomized treatments
. label define randvig1 1 "small N, binary FI, binary SS" 2 "large N, binary FI, binary SS" 3 "sma
> ll N, binary FI, continuous SS" 4 "large N, binary FI, continuous SS" 5 "small N, continuous FI,
>  binary SS" 6 "large N, continuous FI, binary SS" 7 "small N, continuous FI, continuous SS " 8 "
> large N, continuous FI, continuous SS"

. label values rand1 randvig1

. label define randvig2 1 "extensively studied" 2 "not extensively studied"

. label values rand2 randvig2

. label define randvig3 1 "small N" 2 "large N"

. label values rand3 randvig3

. label define randvig4 1 "small N" 2 "large N"

. label values rand4 randvig4

. 
. 
. *label manipulation checks
. label variable Manipulate1 "Y-centric"

. label variable Manipulate2 "large N"

. label variable Manipulate3 "measurement level"

. 
. *label attention checks
. label variable attention1 "Globalization and welfare state"

. label variable attention2 "Regime form and interstate war"

. label variable attention3 "Economic development and democracy"

. label variable attention4 "Civil society and elections"

. label variable attention5 "Climate change and economic development"

. 
. *label culture variables
. label variable numberofcases "number of cases"

. 
. label variable stats "Statistical analysis"

. label variable QCA "QCA"

. label variable process "Process Tracing"

. label variable interpret "Interpretivist qualitative methods"

. label variable experiment "Experiments"

. label variable othermeth "Other"

. 
. *relabel outcome variables
. ***Vignette1
. *vig1opt1: "If foreign investment in a country changes from low to high, it gets more likely tha
> t social spending is high"
. gen vig1opt1=.
(820 missing values generated)

. replace vig1opt1=1 if vig1out2V2SQ002=="Yes"
(93 real changes made)

. replace vig1opt1=0 if lastpage>4 & vig1opt1==.
(519 real changes made)

. 
. *vig1opt2: "If foreign investment in a country is high, then social spending is high"
. gen vig1opt2=.
(820 missing values generated)

. replace vig1opt2=1 if vig1out2V2SQ003=="Yes"
(265 real changes made)

. replace vig1opt2=0 if lastpage>4 & vig1opt2==.
(347 real changes made)

. 
. *vig1opt3: "If foreign investment in a country changes from low to high, it gets more likely tha
> t social spending increases"
. gen vig1opt3=.
(820 missing values generated)

. replace vig1opt3=1 if vig1out2V2SQ004=="Yes"
(99 real changes made)

. replace vig1opt3=0 if lastpage>4 & vig1opt3==.
(513 real changes made)

. 
. *vig1opt4: "As foreign investment increases, it gets more likely that social spending changes fr
> om low to high and as foreign investment decreases, it gets more likely that social spending cha
> nges from high to low"
. gen vig1opt4=.
(820 missing values generated)

. replace vig1opt4=1 if vig1out2V2SQ005=="Yes"
(123 real changes made)

. replace vig1opt4=0 if lastpage>4 & vig1opt4==.
(489 real changes made)

. 
. *vig1opt5: "If foreign investment in a country is high, then social spending is high and if fore
> ign investment in a country is low, then social spending is low"
. gen vig1opt5=.
(820 missing values generated)

. replace vig1opt5=1 if vig1out2V2SQ006=="Yes"
(302 real changes made)

. replace vig1opt5=0 if lastpage>4 & vig1opt5==.
(310 real changes made)

. 
. *vig1opt6: "As foreign investment increases, it gets more likely that social spending increases 
> and as foreign investment decreases, it gets more likely that social spending decreases"
. gen vig1opt6=.
(820 missing values generated)

. replace vig1opt6=1 if vig1out2V2SQ007=="Yes"
(182 real changes made)

. replace vig1opt6=0 if lastpage>4 & vig1opt6==.
(430 real changes made)

. 
. *vig1opt7: "If foreign investment in a country is low, then social spending is low"
. gen vig1opt7=.
(820 missing values generated)

. replace vig1opt7=1 if vig1out2V2SQ008=="Yes"
(251 real changes made)

. replace vig1opt7=0 if lastpage>4 & vig1opt7==.
(361 real changes made)

. drop vig1out2V2*

. 
. **Vignette2
. replace vig2out2="A1" if vig2out2=="You conduct an exploratory analysis to identify the causes o
> f the phenomenon"
(350 real changes made)

. replace vig2out2="A2" if vig2out2=="You conduct a confirmatory study to evaluate how particular 
> factors on average influence the phenomenon"
(186 real changes made)

. replace vig2out2="A3" if vig2out2=="You conduct a confirmatory study that traces the process of 
> how particular factors influence the phenomenon within individual cases"
(186 real changes made)

. gen vig2outcome=.
(820 missing values generated)

. replace vig2outcome=1 if vig2out2=="A1" 
(350 real changes made)

. replace vig2outcome=2 if vig2out2=="A2" 
(186 real changes made)

. replace vig2outcome=3 if vig2out2=="A3" 
(186 real changes made)

. label define vig2outcome 1 "exploratory" 2 "confirmatory" 3 "process"

. label values vig2outcome vig2outcome

. drop vig2out2

. 
. **Vignette3
. *vig3opt1: "Democratic dyad is a sufficient condition for interstate peace"
. gen vig3opt1=.
(820 missing values generated)

. replace vig3opt1=1 if vig3out2V2SQ001=="Yes"
(175 real changes made)

. replace vig3opt1=0 if vig3out2V2SQ001=="No"
(401 real changes made)

. replace vig3opt1=. if vig3out2V2SQ001=="N/A"
(0 real changes made)

. *vig3opt2: "Nondemocratic dyad is a necessary condition for war between states"
. gen vig3opt2=.
(820 missing values generated)

. replace vig3opt2=1 if vig3out2V2SQ002=="Yes"
(186 real changes made)

. replace vig3opt2=0 if vig3out2V2SQ002=="No"
(390 real changes made)

. replace vig3opt2=. if vig3out2V2SQ002=="N/A"
(0 real changes made)

. *vig3opt3: "If two states are democratic, interstate war between them is less likely"
. gen vig3opt3=.
(820 missing values generated)

. replace vig3opt3=1 if vig3out2V2SQ003=="Yes"
(488 real changes made)

. replace vig3opt3=0 if vig3out2V2SQ003=="No"
(88 real changes made)

. replace vig3opt3=. if vig3out2V2SQ003=="N/A"
(0 real changes made)

. drop vig3out2V2*

. 
. **Vignette4: Note that the ordering of outcome statement is different for EU and US
. *vig4opt1: "Economic development is necessary to achieve democracy"
. gen vig4opt1=.
(820 missing values generated)

. replace vig4opt1=1 if vig4out2V2SQ001=="Yes"
(6 real changes made)

. replace vig4opt1=0 if lastpage>7 & vig4opt1==.
(556 real changes made)

. *vig4opt2: "The level of economic development is positively related to the level of democracy"
. gen vig4opt2=.
(820 missing values generated)

. replace vig4opt2=1 if vig4out2V2SQ002=="Yes"
(557 real changes made)

. replace vig4opt2=0 if lastpage>7 & vig4opt2==.
(5 real changes made)

. drop vig4out2V2SQ*

. 
. save "data/USAfull4.dta", replace
file data/USAfull4.dta saved

. 
. 
. ***combine data
. append using "data/EUfull4.dta", generate(mark)
(note: variable numberofcases was str352, now str405 to accommodate using data's values)
(note: variable othermethod was str150, now str337 to accommodate using data's values)
(note: variable status was str19, now str21 to accommodate using data's values)
(note: variable statusother was str44, now str71 to accommodate using data's values)
(label vig2outcome already defined)
(label randvig4 already defined)
(label randvig3 already defined)
(label randvig2 already defined)
(label randvig1 already defined)

. save "data/fulldata.dta", replace
file data/fulldata.dta saved

. import excel "data/textdata/EUtextdata.xls", sheet("Sheet1") firstrow clear

. save "data/EUtextdata.dta", replace
file data/EUtextdata.dta saved

. import excel "data/textdata/UStextdata.xls", sheet("Sheet1") firstrow clear

. save "data/UStextdata.dta", replace
file data/UStextdata.dta saved

. append using "data/EUtextdata.dta"

. save "data/textdata.dta", replace
file data/textdata.dta saved

. use "data/fulldata.dta", clear

. merge 1:1 country id using "data/textdata.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                           325
        from master                       325  (_merge==1)
        from using                          0  (_merge==2)

    matched                             1,401  (_merge==3)
    -----------------------------------------

. drop _m

. save "data/fulldata.dta", replace
file data/fulldata.dta saved

. 
. ***Prepare coding of "number of cases"
. use "data/fulldata.dta", clear

. destring numberofcases, generate(numberofcases2) force
numberofcases: contains nonnumeric characters; numberofcases2 generated as long
(1604 missing values generated)

. export excel country id numberofcases using "data/numberofcases.xls" if numberofcases2==., first
> row(variables) replace
file data/numberofcases.xls saved

. save "data/fulldata.dta", replace
file data/fulldata.dta saved

. **import coded data
. import excel "data/numberofcasescoded.xls", sheet("Sheet1") firstrow clear

. rename numberofcases_coded casescoded

. *final adjustments // 1: small-n, 2: large-n, 3: mixed-method
. *Note that answers of the kind "it depends on the research question" were coded as missing
. replace casescode=2 if country=="USA" & id==520
(1 real change made)

. replace casescode=2 if country=="USA" & id==200
(1 real change made)

. replace casescode=2 if country=="USA" & id==223
(1 real change made)

. replace casescode=2 if country=="DE" &  id==91
(1 real change made)

. replace casescode=2 if country=="USA" & id==297
(1 real change made)

. replace casescode=2 if country=="DE" &  id==142
(1 real change made)

. replace casescode=2 if country=="USA" & id==477
(1 real change made)

. replace casescode=3 if country=="UK" &  id==323
(1 real change made)

. replace casescode=2 if country=="UK" &  id==277
(1 real change made)

. replace casescode=2 if country=="USA" & id==162
(1 real change made)

. replace casescode=2 if country=="USA" & id==144
(1 real change made)

. replace casescode=2 if country=="CH" &  id==124
(1 real change made)

. replace casescode=1 if country=="USA" & id==668
(1 real change made)

. replace casescode=2 if country=="USA" & id==777
(1 real change made)

. replace casescode=2 if country=="SE" &  id==33
(1 real change made)

. replace casescode=2 if country=="SE" &  id==75
(1 real change made)

. replace casescode=2 if country=="UK" &  id==153
(1 real change made)

. replace casescode=1 if country=="IT" &  id==42
(1 real change made)

. replace casescode=3 if country=="NL" &  id==89
(1 real change made)

. replace casescode=2 if country=="UK" &  id==112
(1 real change made)

. replace casescode=2 if country=="USA" & id==495
(1 real change made)

. replace casescode=2 if country=="USA" & id==343
(1 real change made)

. replace casescode=2 if country=="USA" & id==213
(1 real change made)

. drop numberofcases

. rename casescoded numberofcases

. save "data/numberofcasescoded.dta", replace
file data/numberofcasescoded.dta saved

. 
. *combine numeric & coded data
. use "data/fulldata.dta", clear

. drop numberofcases

. merge 1:1 country id using "data/numberofcasescoded.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                           122
        from master                       122  (_merge==1)
        from using                          0  (_merge==2)

    matched                             1,604  (_merge==3)
    -----------------------------------------

. drop _m

. ****Define categorical variable for number of cases
. replace numberofcases=1 if numberofcases2 <51 & numberofcases2 !=.
(58 real changes made)

. replace numberofcases=2 if numberofcases2 >50 & numberofcases2 !=.
(64 real changes made)

. label define numberofcases 1 "small N" 2 "large N" 3 "mixed-method" 99 "not applicable"

. label values numberofcases numberofcases

. save "data/fulldata.dta", replace
file data/fulldata.dta saved

. 
. 
. ***Prepare coding of "othermethod"
. use "data/fulldata.dta", clear

. export excel country id othermethod using "data/othermethod.xls" if othermethod!="", firstrow(va
> riables) replace
file data/othermethod.xls saved

. drop othermethod

. save "data/fulldata.dta", replace
file data/fulldata.dta saved

. import excel "data/othermethodcoded.xls", sheet("Sheet1") firstrow clear

. save "data/othermethodcoded.dta", replace
file data/othermethodcoded.dta saved

. use "data/fulldata.dta", clear

. merge 1:1 country id using "data/othermethodcoded.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                         1,544
        from master                     1,544  (_merge==1)
        from using                          0  (_merge==2)

    matched                               182  (_merge==3)
    -----------------------------------------

. drop _m

. **Define categorical variable for method preferences
. gen qualmeth=.
(1,726 missing values generated)

. gen quantmeth=.
(1,726 missing values generated)

. gen mixedmeth=.
(1,726 missing values generated)

. replace qualmeth=1 if QCA=="Yes" | process =="Yes" | interpret =="Yes" | othermethodcat=="1"
(649 real changes made)

. replace quantmeth=1 if stats=="Yes" | experiment =="Yes" | othermethodcat=="2"
(852 real changes made)

. replace mixedmeth=1 if qualmeth==1 & quantmeth==1 | othermethodcat=="3"
(452 real changes made)

. gen methodcat=.
(1,726 missing values generated)

. replace methodcat=1 if qualmeth==1 & quantmeth==. & mixedmeth==.
(200 real changes made)

. replace methodcat=2 if qualmeth==. & quantmeth==1 & mixedmeth==.
(400 real changes made)

. replace methodcat=3 if qualmeth==1 & quantmeth==1 | mixedmeth==1
(452 real changes made)

. label define methodcat 1 "small N" 2 "large N" 3 "mixed-method" 99 "not applicable"

. label values methodcat methodcat

. save "data/fulldata.dta", replace
file data/fulldata.dta saved

. 
. ***Prepare coding of "status"
. use "data/fulldata.dta", clear

. export excel country id statusother using "data/statusother.xls" if statusother!="", firstrow(va
> riables) replace
file data/statusother.xls saved

. import excel "data/statusothercoded.xls", sheet("Sheet1") firstrow clear

. save "data/statusothercoded.dta", replace
file data/statusothercoded.dta saved

. use "data/fulldata.dta", clear

. merge 1:1 country id using "data/statusothercoded.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                         1,615
        from master                     1,615  (_merge==1)
        from using                          0  (_merge==2)

    matched                               111  (_merge==3)
    -----------------------------------------

. drop _m

. ***Define categorical status variable
. destring statuscat, replace force
statuscat: all characters numeric; replaced as byte
(1672 missing values generated)

. replace statuscat= 1 if status=="Associate Professor" | status=="Full Professor"
(242 real changes made)

. replace statuscat= 2 if status=="Postdoc" | status=="Assistant Professor"
(308 real changes made)

. replace statuscat=3 if status=="Graduate Student"
(456 real changes made)

. label define statuscat 1 "Professor" 2 "Senior Scholar" 3 "Junior Scholar" 99 "not applicable"

. label values statuscat statuscat

. save "data/fulldata.dta", replace
file data/fulldata.dta saved

. 
. ***Define categorical variable for research cultures
. gen culture=.
(1,726 missing values generated)

. replace culture=1 if  methodcat==1 & numberofcases==1
(160 real changes made)

. replace culture=2 if  methodcat==2 & numberofcases==2
(334 real changes made)

. replace culture=3 if  methodcat==3 | numberofcases==3
(495 real changes made)

. label define culture 1 "Qualitative" 2 "Quantitative" 3 "Mixed-method" 99 "not applicable"

. label values culture culture

. save "data/fulldata.dta", replace
file data/fulldata.dta saved

. 
. ***encode string variables
. encode country, gen(countrynew)

. encode sex, gen(gender)

. label variable gender "gender"

. *encode attention1, gen(attention1n)
. *encode attention2, gen(attention2n)
. *encode attention3, gen(attention3n)
. *encode attention4, gen(attention4n)
. *encode attention5, gen(attention5n)
. gen Manipulate1n=0

. replace Manipulate1n=1 if Manipulate1=="Yes"
(72 real changes made)

. replace  Manipulate1n=. if Manipulate1=="N/A"
(661 real changes made, 661 to missing)

. gen Manipulate2n=0

. replace Manipulate2n=1 if Manipulate2=="Yes"
(668 real changes made)

. replace  Manipulate2n=. if Manipulate2=="N/A"
(661 real changes made, 661 to missing)

. gen Manipulate3n=0

. replace Manipulate3n=1 if Manipulate3=="Yes"
(541 real changes made)

. replace  Manipulate3n=. if Manipulate3=="N/A"
(661 real changes made, 661 to missing)

. 
. label variable Manipulate1n "Y-centric"

. label variable Manipulate2n "large N"

. label variable Manipulate3n "measurement level"

. 
. label define manipulate 0 "No" 1 "Yes"

. label values Manipulate1n manipulate

. label values Manipulate2n manipulate

. label values Manipulate3n manipulate

. 
. ***generate categorical variables from codings of open-ended questions
. *vig1
. gen vig1txtn=.
(1,726 missing values generated)

. replace vig1txtn=1 if vig1txt=="s"
(23 real changes made)

. replace vig1txtn=2 if vig1txt=="c"
(801 real changes made)

. replace vig1txtn=3 if vig1txt=="b"
(1 real change made)

. replace vig1txtn=99 if vig1txt=="u"
(389 real changes made)

. drop vig1txt

. rename vig1txtn vig1txt

. gen vig1set=0

. replace vig1set=1 if vig1txt==1
(23 real changes made)

. label define set 0 "not set-theoretical" 1 "set-theoretical" 

. label values vig1set set

. gen vig1corr=0

. replace vig1corr=1 if vig1txt==2
(801 real changes made)

. label define corr 0 "not correlational" 1 "correlational" 

. label values vig1corr corr

. *vig3
. gen vig3txtn=.
(1,726 missing values generated)

. replace vig3txtn=1 if vig3txt=="s"
(304 real changes made)

. replace vig3txtn=2 if vig3txt=="c"
(502 real changes made)

. replace vig3txtn=3 if vig3txt=="b"
(38 real changes made)

. replace vig3txtn=99 if vig3txt=="u"
(235 real changes made)

. drop vig3txt

. rename vig3txtn vig3txt

. gen vig3set=0

. replace vig3set=1 if vig3txt==1
(304 real changes made)

. label values vig3set set

. gen vig3corr=0

. replace vig3corr=1 if vig3txt==2
(502 real changes made)

. label values vig3corr corr

. *vig4
. gen vig4txtn=.
(1,726 missing values generated)

. replace vig4txtn=1 if vig4txt=="s"
(11 real changes made)

. replace vig4txtn=2 if vig4txt=="c"
(807 real changes made)

. replace vig4txtn=3 if vig4txt=="b"
(5 real changes made)

. replace vig4txtn=99 if vig4txt=="u"
(236 real changes made)

. drop vig4txt

. rename vig4txtn vig4txt

. gen vig4set=0

. replace vig4set=1 if vig4txt==1
(11 real changes made)

. label values vig4set set

. gen vig4corr=0

. replace vig4corr=1 if vig4txt==2
(807 real changes made)

. label values vig4corr corr

. label define vigtxt 1 "set-theoretical" 2 "correlational" 3 "both" 99 "unclear"

. label values vig1txt vigtxt

. label values vig3txt vigtxt

. label values vig4txt vigtxt

. 
. save "data/fulldata.dta", replace
file data/fulldata.dta saved

. 
. *gen dummy variables for experiment 2
. gen vig1largeN=0

. replace vig1largeN=1 if rand1==2 | rand1==4 | rand1==6 | rand1==8
(899 real changes made)

. label define largeN 0 "small N" 1 "large N" 

. label values vig1largeN largeN

. 
. gen vig1continouscause=0

. replace vig1continouscause=1 if rand1==5 | rand1==6 | rand1==7 | rand1==8
(867 real changes made)

. label define causemeasure 0 "binary cause" 1 "continous cause" 

. label values vig1continouscause causemeasure

. 
. gen vig1continousoutcome=0

. replace vig1continousoutcome=1 if rand1==3 | rand1==4 | rand1==7 | rand1==8
(893 real changes made)

. label define effectmeasure 0 "binary effect" 1 "continous effect" 

. label values vig1continousoutcome effectmeasure

. 
. 
. *Whereas statements 1-3 indicate a set-theoretic perspective on causality, the statements 4-7 im
> ply a correlational causal reasoning.
. gen vig1closedset=0

. replace vig1closedset=1 if vig1opt2==1 | vig1opt5==1 | vig1opt7==1 
(757 real changes made)

. gen vig1closedcorr=0

. replace vig1closedcorr=1 if vig1opt1==1 | vig1opt3==1 | vig1opt4==1 | vig1opt6==1 
(557 real changes made)

. 
. *generate count variables of the number of set or correlation answers for experiment 2
. gen vig1closedsetcount=.
(1,726 missing values generated)

. replace vig1closedsetcount= vig1opt2 + vig1opt5 + vig1opt7
(1,194 real changes made)

. gen vig1closedcorrcount=.
(1,726 missing values generated)

. replace vig1closedcorrcount= vig1opt1 + vig1opt3 + vig1opt4 + vig1opt6
(1,193 real changes made)

. 
. *generate preference variable
. gen vig1pref=vig1closedsetcount-vig1closedcorrcount
(533 missing values generated)

. 
. *label statment variables and generate alternative variable names
. label variable vig1opt1 "corr. statement 1"

. label variable vig1opt2 "set statement 1"

. label variable vig1opt3 "corr. statement 2"

. label variable vig1opt4 "corr. statement 3"

. label variable vig1opt5 "set statement 2"

. label variable vig1opt6 "corr. statement 4"

. label variable vig1opt7 "set statement 3"

. 
. gen corr1=vig1opt1
(533 missing values generated)

. gen corr2=vig1opt3
(532 missing values generated)

. gen corr3=vig1opt4
(532 missing values generated)

. gen corr4=vig1opt6
(532 missing values generated)

. gen set1=vig1opt2
(532 missing values generated)

. gen set2=vig1opt5
(532 missing values generated)

. gen set3=vig1opt7
(532 missing values generated)

. 
. **generate age groups defined by APSA
. egen agecat = cut(age), at(0,25,35,45,55,65,75,100) label
(665 missing values generated)

. 
. *gen EU/US groups
. gen continent="EU"

. replace continent="US" if country=="USA"
(820 real changes made)

. encode continent, gen(continent2)

. drop continent

. rename continent2 continent

. 
. *gen answer combinations for vig3
. egen label3 = concat(vig3opt?) if vig3opt1 !=.
(631 missing values generated)

. egen group3 = group(label3), label
(631 missing values generated)

. label define vig3outcat 1 "not sufficient, not necessary, not likely" 2 "not sufficient, not nec
> essary, likely" 3 "not sufficient, necessary, not likely" 4 "not sufficient, necessary, likely" 
> 5 "sufficient, not necessary, not likely" 6 "sufficient, not necessary, likely" 7 "sufficient, n
> ecessary, not likely" 8 "sufficient, necessary, likely" 

. label values group3 vig3outcat  

. decode group3, gen(vig3outcat)

. *gen dummies for answer combinations
. tab vig3outcat, gen(vig3outcatdum)

                          group(label3) |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
      not sufficient, necessary, likely |         65        5.94        5.94
  not sufficient, necessary, not likely |         73        6.67       12.60
  not sufficient, not necessary, likely |        566       51.69       64.29
not sufficient, not necessary, not li.. |          4        0.37       64.66
          sufficient, necessary, likely |        150       13.70       78.36
      sufficient, necessary, not likely |        113       10.32       88.68
      sufficient, not necessary, likely |         69        6.30       94.98
  sufficient, not necessary, not likely |         55        5.02      100.00
----------------------------------------+-----------------------------------
                                  Total |      1,095      100.00

. 
. *gen answer combinations for vig4
. egen label4 = concat(vig4opt?) if vig4opt1 !=.
(656 missing values generated)

. egen group4 = group(label4), label
(656 missing values generated)

. label define vig4outcat 1 "not set-theoretic, not correlational" 2 "not set-theoretic, correlati
> onal" 3 "set-theoretic, not correlational" 4 "set-theoretic, correlational" 

. label values group4 vig4outcat  

. decode group4, gen(vig4outcat)

. *gen dummies for answer combinations
. tab vig4outcat, gen(vig4outcatdum)

                       group(label4) |      Freq.     Percent        Cum.
-------------------------------------+-----------------------------------
    not set-theoretic, correlational |      1,037       96.92       96.92
not set-theoretic, not correlational |          2        0.19       97.10
        set-theoretic, correlational |         19        1.78       98.88
    set-theoretic, not correlational |         12        1.12      100.00
-------------------------------------+-----------------------------------
                               Total |      1,070      100.00

. 
. 
. *gen mulinominal outcome measures from open-ended questions
. *exclude open-ended answers that feature both correlational and set-theoretical reasoning 
. gen vig1txt2=vig1txt
(512 missing values generated)

. gen vig3txt2=vig3txt
(647 missing values generated)

. gen vig4txt2=vig4txt
(667 missing values generated)

. replace vig1txt2=. if vig1txt==3
(1 real change made, 1 to missing)

. replace vig3txt2=. if vig3txt==3
(38 real changes made, 38 to missing)

. replace vig4txt2=. if vig4txt==3
(5 real changes made, 5 to missing)

. egen vig1open = group(vig1txt2), label
(513 missing values generated)

. egen vig3open = group(vig3txt2), label
(685 missing values generated)

. egen vig4open = group(vig4txt2), label
(672 missing values generated)

. label define vigopen 1 "set-theoretical" 2 "correlational" 3 "unclear"

. label values vig1open vigopen  

. label values vig3open vigopen  

. label values vig4open vigopen  

. 
. save "data/fulldata.dta", replace
file data/fulldata.dta saved

. 
. ***import data on response rates
. import delimited "data/responserate.csv", varnames(1) encoding(utf8) clear 
(4 vars, 11 obs)

. save "data/responserate.dta", replace
file data/responserate.dta saved

. 
end of do-file

. do "code/2analysis.do"

. clear

. 
. use "data/fulldata.dta", clear

. 
. ***Figure 1: Flow-chart
. keep if lastpage==17 
(679 observations deleted)

. tab continent

  continent |      Freq.     Percent        Cum.
------------+-----------------------------------
         EU |        504       48.14       48.14
         US |        543       51.86      100.00
------------+-----------------------------------
      Total |      1,047      100.00

. tab rand2

                  rand2 |      Freq.     Percent        Cum.
------------------------+-----------------------------------
    extensively studied |        522       49.86       49.86
not extensively studied |        525       50.14      100.00
------------------------+-----------------------------------
                  Total |      1,047      100.00

. tab vig2outcome

 vig2outcome |      Freq.     Percent        Cum.
-------------+-----------------------------------
 exploratory |        504       48.14       48.14
confirmatory |        254       24.26       72.40
     process |        289       27.60      100.00
-------------+-----------------------------------
       Total |      1,047      100.00

. keep if attention1=="Yes" & attention2=="Yes" & attention3=="Yes" & attention4=="No" & attention
> 5=="No"
(79 observations deleted)

. tab culture

       culture |      Freq.     Percent        Cum.
---------------+-----------------------------------
   Qualitative |        150       16.63       16.63
  Quantitative |        309       34.26       50.89
  Mixed-method |        443       49.11      100.00
---------------+-----------------------------------
         Total |        902      100.00

. tab continent if culture !=.

  continent |      Freq.     Percent        Cum.
------------+-----------------------------------
         EU |        436       48.34       48.34
         US |        466       51.66      100.00
------------+-----------------------------------
      Total |        902      100.00

. 
. *Define sample 
. mlogit vig2outcome i.rand2 i.culture i.gender i.statuscat age i.continent, robust cluster(countr
> y)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -783.16904  
Iteration 2:   log pseudolikelihood = -782.05136  
Iteration 3:   log pseudolikelihood = -782.04694  
Iteration 4:   log pseudolikelihood = -782.04694  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -782.04694               Pseudo R2         =     0.1796

                                           (Std. Err. adjusted for 11 clusters in country)
------------------------------------------------------------------------------------------
                         |               Robust
             vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
exploratory              |  (base outcome)
-------------------------+----------------------------------------------------------------
confirmatory             |
                   rand2 |
not extensively studied  |  -2.287053   .1628943   -14.04   0.000     -2.60632   -1.967786
                         |
                 culture |
           Quantitative  |   1.635796   .3610655     4.53   0.000     .9281209    2.343471
           Mixed-method  |   1.217204   .3238476     3.76   0.000     .5824739    1.851933
                         |
                  gender |
                   Male  |   .3066359    .198288     1.55   0.122    -.0820014    .6952733
   Prefer not to answer  |   .1574403   .4869327     0.32   0.746    -.7969303    1.111811
                         |
               statuscat |
         Senior Scholar  |   .2202332   .6167684     0.36   0.721    -.9886107    1.429077
         Junior Scholar  |  -.2231455   .8949445    -0.25   0.803    -1.977205    1.530913
                         |
                     age |  -.0085603   .0263573    -0.32   0.745    -.0602197    .0430992
                         |
               continent |
                     US  |  -.2016951   .1837558    -1.10   0.272    -.5618498    .1584596
                   _cons |  -.4791907   1.626606    -0.29   0.768    -3.667279    2.708898
-------------------------+----------------------------------------------------------------
process                  |
                   rand2 |
not extensively studied  |   -2.56267   .3431025    -7.47   0.000    -3.235138   -1.890201
                         |
                 culture |
           Quantitative  |   -.412419   .2766265    -1.49   0.136     -.954597     .129759
           Mixed-method  |  -.0291203   .2558005    -0.11   0.909      -.53048    .4722395
                         |
                  gender |
                   Male  |   .0240663   .1412343     0.17   0.865    -.2527479    .3008805
   Prefer not to answer  |   .1780907    .571974     0.31   0.756    -.9429577    1.299139
                         |
               statuscat |
         Senior Scholar  |   .5361777   .6323917     0.85   0.397    -.7032872    1.775643
         Junior Scholar  |   .5925152   .7471099     0.79   0.428    -.8717932    2.056824
                         |
                     age |   .0191643   .0221424     0.87   0.387    -.0242341    .0625627
                         |
               continent |
                     US  |   -.383747   .1449531    -2.65   0.008    -.6678498   -.0996442
                   _cons |  -.0382924   1.531226    -0.03   0.980     -3.03944    2.962855
------------------------------------------------------------------------------------------

. keep if e(sample)
(67 observations deleted)

. 
. *Figure 2: Marginal Effects
. mlogit vig2outcome i.rand2 i.culture i.gender i.statuscat age i.continent, robust cluster(countr
> y)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -783.16904  
Iteration 2:   log pseudolikelihood = -782.05136  
Iteration 3:   log pseudolikelihood = -782.04694  
Iteration 4:   log pseudolikelihood = -782.04694  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -782.04694               Pseudo R2         =     0.1796

                                           (Std. Err. adjusted for 11 clusters in country)
------------------------------------------------------------------------------------------
                         |               Robust
             vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
exploratory              |  (base outcome)
-------------------------+----------------------------------------------------------------
confirmatory             |
                   rand2 |
not extensively studied  |  -2.287053   .1628943   -14.04   0.000     -2.60632   -1.967786
                         |
                 culture |
           Quantitative  |   1.635796   .3610655     4.53   0.000     .9281209    2.343471
           Mixed-method  |   1.217204   .3238476     3.76   0.000     .5824739    1.851933
                         |
                  gender |
                   Male  |   .3066359    .198288     1.55   0.122    -.0820014    .6952733
   Prefer not to answer  |   .1574403   .4869327     0.32   0.746    -.7969303    1.111811
                         |
               statuscat |
         Senior Scholar  |   .2202332   .6167684     0.36   0.721    -.9886107    1.429077
         Junior Scholar  |  -.2231455   .8949445    -0.25   0.803    -1.977205    1.530913
                         |
                     age |  -.0085603   .0263573    -0.32   0.745    -.0602197    .0430992
                         |
               continent |
                     US  |  -.2016951   .1837558    -1.10   0.272    -.5618498    .1584596
                   _cons |  -.4791907   1.626606    -0.29   0.768    -3.667279    2.708898
-------------------------+----------------------------------------------------------------
process                  |
                   rand2 |
not extensively studied  |   -2.56267   .3431025    -7.47   0.000    -3.235138   -1.890201
                         |
                 culture |
           Quantitative  |   -.412419   .2766265    -1.49   0.136     -.954597     .129759
           Mixed-method  |  -.0291203   .2558005    -0.11   0.909      -.53048    .4722395
                         |
                  gender |
                   Male  |   .0240663   .1412343     0.17   0.865    -.2527479    .3008805
   Prefer not to answer  |   .1780907    .571974     0.31   0.756    -.9429577    1.299139
                         |
               statuscat |
         Senior Scholar  |   .5361777   .6323917     0.85   0.397    -.7032872    1.775643
         Junior Scholar  |   .5925152   .7471099     0.79   0.428    -.8717932    2.056824
                         |
                     age |   .0191643   .0221424     0.87   0.387    -.0242341    .0625627
                         |
               continent |
                     US  |   -.383747   .1449531    -2.65   0.008    -.6678498   -.0996442
                   _cons |  -.0382924   1.531226    -0.03   0.980     -3.03944    2.962855
------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2 culture) pr(out(1)) post

Average marginal effects                        Number of obs     =        901
Model VCE    : Robust

Expression   : Pr(vig2outcome==exploratory), predict(out(1))
dy/dx w.r.t. : 2.rand2 2.culture 3.culture

------------------------------------------------------------------------------------------
                         |            Delta-method
                         |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                   rand2 |
not extensively studied  |   .5351975   .0373787    14.32   0.000     .4619366    .6084584
                         |
                 culture |
           Quantitative  |  -.0709937   .0446427    -1.59   0.112    -.1584918    .0165043
           Mixed-method  |  -.0615439   .0396652    -1.55   0.121    -.1392863    .0161986
------------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, xline(0, lpattern(dash) lcolor(black)) title(Exploratory, size(small)) xscale(range(0.
> 6)) xlabel(-0.6(0.1)0.6) nodraw mcolor(black) mfcolor(black) msymbol(diamond)  ciopts(lcolor(bla
> ck))

. graph save Graph "output/fig2m1.gph", replace
(file output/fig2m1.gph saved)

. mlogit vig2outcome i.rand2 i.culture i.gender i.statuscat age i.continent, robust cluster(countr
> y)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -783.16904  
Iteration 2:   log pseudolikelihood = -782.05136  
Iteration 3:   log pseudolikelihood = -782.04694  
Iteration 4:   log pseudolikelihood = -782.04694  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -782.04694               Pseudo R2         =     0.1796

                                           (Std. Err. adjusted for 11 clusters in country)
------------------------------------------------------------------------------------------
                         |               Robust
             vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
exploratory              |  (base outcome)
-------------------------+----------------------------------------------------------------
confirmatory             |
                   rand2 |
not extensively studied  |  -2.287053   .1628943   -14.04   0.000     -2.60632   -1.967786
                         |
                 culture |
           Quantitative  |   1.635796   .3610655     4.53   0.000     .9281209    2.343471
           Mixed-method  |   1.217204   .3238476     3.76   0.000     .5824739    1.851933
                         |
                  gender |
                   Male  |   .3066359    .198288     1.55   0.122    -.0820014    .6952733
   Prefer not to answer  |   .1574403   .4869327     0.32   0.746    -.7969303    1.111811
                         |
               statuscat |
         Senior Scholar  |   .2202332   .6167684     0.36   0.721    -.9886107    1.429077
         Junior Scholar  |  -.2231455   .8949445    -0.25   0.803    -1.977205    1.530913
                         |
                     age |  -.0085603   .0263573    -0.32   0.745    -.0602197    .0430992
                         |
               continent |
                     US  |  -.2016951   .1837558    -1.10   0.272    -.5618498    .1584596
                   _cons |  -.4791907   1.626606    -0.29   0.768    -3.667279    2.708898
-------------------------+----------------------------------------------------------------
process                  |
                   rand2 |
not extensively studied  |   -2.56267   .3431025    -7.47   0.000    -3.235138   -1.890201
                         |
                 culture |
           Quantitative  |   -.412419   .2766265    -1.49   0.136     -.954597     .129759
           Mixed-method  |  -.0291203   .2558005    -0.11   0.909      -.53048    .4722395
                         |
                  gender |
                   Male  |   .0240663   .1412343     0.17   0.865    -.2527479    .3008805
   Prefer not to answer  |   .1780907    .571974     0.31   0.756    -.9429577    1.299139
                         |
               statuscat |
         Senior Scholar  |   .5361777   .6323917     0.85   0.397    -.7032872    1.775643
         Junior Scholar  |   .5925152   .7471099     0.79   0.428    -.8717932    2.056824
                         |
                     age |   .0191643   .0221424     0.87   0.387    -.0242341    .0625627
                         |
               continent |
                     US  |   -.383747   .1449531    -2.65   0.008    -.6678498   -.0996442
                   _cons |  -.0382924   1.531226    -0.03   0.980     -3.03944    2.962855
------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2 culture) pr(out(2)) post

Average marginal effects                        Number of obs     =        901
Model VCE    : Robust

Expression   : Pr(vig2outcome==confirmatory), predict(out(2))
dy/dx w.r.t. : 2.rand2 2.culture 3.culture

------------------------------------------------------------------------------------------
                         |            Delta-method
                         |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                   rand2 |
not extensively studied  |  -.2175663   .0214606   -10.14   0.000    -.2596283   -.1755042
                         |
                 culture |
           Quantitative  |   .2559718   .0406831     6.29   0.000     .1762343    .3357093
           Mixed-method  |   .1453008   .0312177     4.65   0.000     .0841151    .2064865
------------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, scheme(s1mono) xline(0, lpattern(dash) lcolor(black)) title(Confirmatory, size(small))
>  xscale(range(0.6)) xlabel(-0.6(0.1)0.6) nodraw mcolor(black) mfcolor(black) msymbol(diamond) ci
> opts(lcolor(black))

. graph save Graph "output/fig2m2.gph", replace
(file output/fig2m2.gph saved)

. mlogit vig2outcome i.rand2 i.culture i.gender i.statuscat age i.continent, robust cluster(countr
> y)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -783.16904  
Iteration 2:   log pseudolikelihood = -782.05136  
Iteration 3:   log pseudolikelihood = -782.04694  
Iteration 4:   log pseudolikelihood = -782.04694  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -782.04694               Pseudo R2         =     0.1796

                                           (Std. Err. adjusted for 11 clusters in country)
------------------------------------------------------------------------------------------
                         |               Robust
             vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
exploratory              |  (base outcome)
-------------------------+----------------------------------------------------------------
confirmatory             |
                   rand2 |
not extensively studied  |  -2.287053   .1628943   -14.04   0.000     -2.60632   -1.967786
                         |
                 culture |
           Quantitative  |   1.635796   .3610655     4.53   0.000     .9281209    2.343471
           Mixed-method  |   1.217204   .3238476     3.76   0.000     .5824739    1.851933
                         |
                  gender |
                   Male  |   .3066359    .198288     1.55   0.122    -.0820014    .6952733
   Prefer not to answer  |   .1574403   .4869327     0.32   0.746    -.7969303    1.111811
                         |
               statuscat |
         Senior Scholar  |   .2202332   .6167684     0.36   0.721    -.9886107    1.429077
         Junior Scholar  |  -.2231455   .8949445    -0.25   0.803    -1.977205    1.530913
                         |
                     age |  -.0085603   .0263573    -0.32   0.745    -.0602197    .0430992
                         |
               continent |
                     US  |  -.2016951   .1837558    -1.10   0.272    -.5618498    .1584596
                   _cons |  -.4791907   1.626606    -0.29   0.768    -3.667279    2.708898
-------------------------+----------------------------------------------------------------
process                  |
                   rand2 |
not extensively studied  |   -2.56267   .3431025    -7.47   0.000    -3.235138   -1.890201
                         |
                 culture |
           Quantitative  |   -.412419   .2766265    -1.49   0.136     -.954597     .129759
           Mixed-method  |  -.0291203   .2558005    -0.11   0.909      -.53048    .4722395
                         |
                  gender |
                   Male  |   .0240663   .1412343     0.17   0.865    -.2527479    .3008805
   Prefer not to answer  |   .1780907    .571974     0.31   0.756    -.9429577    1.299139
                         |
               statuscat |
         Senior Scholar  |   .5361777   .6323917     0.85   0.397    -.7032872    1.775643
         Junior Scholar  |   .5925152   .7471099     0.79   0.428    -.8717932    2.056824
                         |
                     age |   .0191643   .0221424     0.87   0.387    -.0242341    .0625627
                         |
               continent |
                     US  |   -.383747   .1449531    -2.65   0.008    -.6678498   -.0996442
                   _cons |  -.0382924   1.531226    -0.03   0.980     -3.03944    2.962855
------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2 culture) pr(out(3)) post

Average marginal effects                        Number of obs     =        901
Model VCE    : Robust

Expression   : Pr(vig2outcome==process), predict(out(3))
dy/dx w.r.t. : 2.rand2 2.culture 3.culture

------------------------------------------------------------------------------------------
                         |            Delta-method
                         |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                   rand2 |
not extensively studied  |  -.3176312   .0435111    -7.30   0.000    -.4029114    -.232351
                         |
                 culture |
           Quantitative  |  -.1849781    .052959    -3.49   0.000    -.2887759   -.0811802
           Mixed-method  |  -.0837569   .0490336    -1.71   0.088     -.179861    .0123471
------------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, scheme(s1mono) xline(0, lpattern(dash) lcolor(black)) title(Process, size(small)) xsca
> le(range(0.6)) xlabel(-0.6(0.1)0.6) nodraw mcolor(black) mfcolor(black) msymbol(diamond) ciopts(
> lcolor(black))

. graph save Graph "output/fig2m3.gph", replace
(file output/fig2m3.gph saved)

. graph combine "output/fig2m1.gph" "output/fig2m2.gph" "output/fig2m3.gph", rows(3) imargin(small
> ) title("") commonscheme graphregion(color(white) margin(small) fcolor(white)) xcommon ycommon

. graph save Graph "output/fig2.gph", replace
(file output/fig2.gph saved)

. graph export "output/figure2.tif", width(1000) replace
(file output/figure2.tif written in TIFF format)

. est clear

. 
. 
. ****APPENDIX*****
. **Descriptive statistics
. *Table A1: Country coverage
. asdoc tab countrynew, title(Table A1: Country coverage) fs(12) replace save(output/tableA1.doc)

 countrynew |      Freq.     Percent        Cum.
------------+-----------------------------------
         BE |          6        0.67        0.67
         CH |         64        7.10        7.77
         DE |         96       10.65       18.42
         DK |         45        4.99       23.42
         FI |          2        0.22       23.64
         IT |         19        2.11       25.75
         NL |         34        3.77       29.52
         NO |         13        1.44       30.97
         SE |         29        3.22       34.18
         UK |        127       14.10       48.28
        USA |        466       51.72      100.00
------------+-----------------------------------
      Total |        901      100.00
Click to Open File:  output/tableA1.doc

. 
. *Table A2: Response rates
. use "data/responserate.dta", clear

. asdoc list, title(Table A1: Response rates) fs(12) replace save(output/tableA2.doc)

     +------------------------------------------+
     | country   invita~s   comple~d   respon~e |
     |------------------------------------------|
  1. |      FI         47          3   6.382979 |
  2. |      NO         79         15   18.98734 |
  3. |      IT         57         22   38.59649 |
  4. |      UK       1691        161   9.520993 |
  5. |      DE        601        108   17.97005 |
     |------------------------------------------|
  6. |      NL        349         37   10.60172 |
  7. |      SE        309         29   9.385114 |
  8. |      CH        357         71   19.88795 |
  9. |      BE        164         10   6.097561 |
 10. |      DK        261         48    18.3908 |
     |------------------------------------------|
 11. |      US       3316        543   16.37515 |
     +------------------------------------------+
Click to Open File:  output/tableA2.doc

. 
. *Table A3: List of Universities
. import delimited "data/EUpart.csv", varnames(1) encoding(utf8) clear 
(1 var, 36 obs)

. asdoc list, title(Table A3a:  List of EU-Universities) fs(12) replace save(output/tableA3.doc)

     +-----------------------------------------------------------+
     |                                                university |
     |-----------------------------------------------------------|
  1. |                                      University of Oxford |
  2. |                                   University of Cambridge |
  3. | ETH Zurich – Swiss Federal Institute of Technology Zurich |
  4. |                                 University College London |
  5. |                                London School of Economics |
     |-----------------------------------------------------------|
  6. |                                   University of Edinburgh |
  7. |                    Ludwig-Maximilians-Universität München |
  8. |                                     King's College London |
  9. |                                                 KU Leuven |
 10. |                                    Universität Heidelberg |
     |-----------------------------------------------------------|
 11. |                            Technische Universität München |
 12. |                                  University of Manchester |
 13. |                            Humboldt-Universität zu Berlin |
 14. |                                   University of Amsterdam |
 15. |                                  Freie Universität Berlin |
     |-----------------------------------------------------------|
 16. |                                         Leiden University |
 17. |                                               RWTH Aachen |
 18. |                                   University of Groningen |
 19. |                                     University of Warwick |
 20. |                                     University of Glasgow |
     |-----------------------------------------------------------|
 21. |                       Eberhard Karls Universität Tübingen |
 22. |                                        Uppsala University |
 23. |                                     Maastricht University |
 24. |                       Albert-Ludwigs-Universität Freiburg |
 25. |                                         Durham University |
     |-----------------------------------------------------------|
 26. |                                           Lund University |
 27. |                                         Aarhus University |
 28. |                                         Universität Basel |
 29. |                                       Universität Zürich  |
 30. |                                      Universität Mannheim |
     |-----------------------------------------------------------|
 31. |                                   University of Sheffield |
 32. |                                   University of Göttingen |
 33. |                                        University of Bonn |
 34. |                                        University of Bern |
 35. |                                  University of St Andrews |
     |-----------------------------------------------------------|
 36. |                           Queen Mary University of London |
     +-----------------------------------------------------------+
Click to Open File:  output/tableA3.doc

. import delimited "data/USpart.csv", varnames(1) encoding(utf8) clear 
(1 var, 30 obs)

. asdoc list, title(Table A3b: List of US-Universities) fs(12) append save(output/tableA3.doc)

     +---------------------------------------------+
     |                                  university |
     |---------------------------------------------|
  1. |                         Stanford University |
  2. |       Massachusetts Institute of Technology |
  3. |                          Harvard University |
  4. |                        Princeton University |
  5. |           University of California, Berkley |
     |---------------------------------------------|
  6. |                   The University of Chicago |
  7. |                             Yale University |
  8. |                  University of Pennsylvania |
  9. |       University of California, Los Angeles |
 10. |                         Columbia University |
     |---------------------------------------------|
 11. |                     John Hopkins University |
 12. |                             Duke University |
 13. |                          Cornell University |
 14. |                     Northwestern University |
 15. |                      University of Michigan |
     |---------------------------------------------|
 16. |                  Carnegie Mellon University |
 17. |                    University of Washington |
 18. |                         New York University |
 19. |  University of Illinois at Urbana-Champaign |
 20. |         University of California, San Diego |
     |---------------------------------------------|
 21. |                     University of Wisconsin |
 22. |     University of California, Santa Barbara |
 23. |               University of Texas at Austin |
 24. |                            Brown University |
 25. |             University of California, Davis |
     |---------------------------------------------|
 26. |                     University of Minnesota |
 27. | University of North Carolina at Chapel Hill |
 28. |          Washington University in St. Louis |
 29. |           University of Southern California |
 30. |                           Boston University |
     +---------------------------------------------+
Click to Open File:  output/tableA3.doc

. 
. *reload data
. use "data/fulldata.dta", clear

. keep if lastpage==17 
(679 observations deleted)

. keep if attention1=="Yes" & attention2=="Yes" & attention3=="Yes" & attention4=="No" & attention
> 5=="No"
(79 observations deleted)

. *Define sample 
. mlogit vig2outcome i.rand2 i.culture i.gender i.statuscat age i.continent, robust cluster(countr
> y)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -783.16904  
Iteration 2:   log pseudolikelihood = -782.05136  
Iteration 3:   log pseudolikelihood = -782.04694  
Iteration 4:   log pseudolikelihood = -782.04694  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -782.04694               Pseudo R2         =     0.1796

                                           (Std. Err. adjusted for 11 clusters in country)
------------------------------------------------------------------------------------------
                         |               Robust
             vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
exploratory              |  (base outcome)
-------------------------+----------------------------------------------------------------
confirmatory             |
                   rand2 |
not extensively studied  |  -2.287053   .1628943   -14.04   0.000     -2.60632   -1.967786
                         |
                 culture |
           Quantitative  |   1.635796   .3610655     4.53   0.000     .9281209    2.343471
           Mixed-method  |   1.217204   .3238476     3.76   0.000     .5824739    1.851933
                         |
                  gender |
                   Male  |   .3066359    .198288     1.55   0.122    -.0820014    .6952733
   Prefer not to answer  |   .1574403   .4869327     0.32   0.746    -.7969303    1.111811
                         |
               statuscat |
         Senior Scholar  |   .2202332   .6167684     0.36   0.721    -.9886107    1.429077
         Junior Scholar  |  -.2231455   .8949445    -0.25   0.803    -1.977205    1.530913
                         |
                     age |  -.0085603   .0263573    -0.32   0.745    -.0602197    .0430992
                         |
               continent |
                     US  |  -.2016951   .1837558    -1.10   0.272    -.5618498    .1584596
                   _cons |  -.4791907   1.626606    -0.29   0.768    -3.667279    2.708898
-------------------------+----------------------------------------------------------------
process                  |
                   rand2 |
not extensively studied  |   -2.56267   .3431025    -7.47   0.000    -3.235138   -1.890201
                         |
                 culture |
           Quantitative  |   -.412419   .2766265    -1.49   0.136     -.954597     .129759
           Mixed-method  |  -.0291203   .2558005    -0.11   0.909      -.53048    .4722395
                         |
                  gender |
                   Male  |   .0240663   .1412343     0.17   0.865    -.2527479    .3008805
   Prefer not to answer  |   .1780907    .571974     0.31   0.756    -.9429577    1.299139
                         |
               statuscat |
         Senior Scholar  |   .5361777   .6323917     0.85   0.397    -.7032872    1.775643
         Junior Scholar  |   .5925152   .7471099     0.79   0.428    -.8717932    2.056824
                         |
                     age |   .0191643   .0221424     0.87   0.387    -.0242341    .0625627
                         |
               continent |
                     US  |   -.383747   .1449531    -2.65   0.008    -.6678498   -.0996442
                   _cons |  -.0382924   1.531226    -0.03   0.980     -3.03944    2.962855
------------------------------------------------------------------------------------------

. keep if e(sample)
(67 observations deleted)

. 
. 
. *Table A4: Distribution of age
. asdoc sum age, title(Table A4: Distribution of age) replace save(output/tableA4.doc)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         age |        901    35.02415     9.39644         22         78
Click to Open File:  output/tableA4.doc

. *Table A5: Gender
. asdoc tab gender, title(Table A5: Gender) replace  save(output/tableA5.doc)

              gender |      Freq.     Percent        Cum.
---------------------+-----------------------------------
              Female |        292       32.41       32.41
                Male |        579       64.26       96.67
Prefer not to answer |         30        3.33      100.00
---------------------+-----------------------------------
               Total |        901      100.00
Click to Open File:  output/tableA5.doc

. *Table A6: Professional status
. asdoc tab statuscat, title(Table A6: Professional status) replace  save(output/tableA6.doc)

     statuscat |      Freq.     Percent        Cum.
---------------+-----------------------------------
     Professor |        202       22.42       22.42
Senior Scholar |        286       31.74       54.16
Junior Scholar |        413       45.84      100.00
---------------+-----------------------------------
         Total |        901      100.00
Click to Open File:  output/tableA6.doc

. *Table A7: Age categories
. asdoc tab agecat, title(Table A7: Age groups) replace  save(output/tableA7.doc)

     agecat |      Freq.     Percent        Cum.
------------+-----------------------------------
         0- |         36        4.00        4.00
        25- |        507       56.27       60.27
        35- |        232       25.75       86.02
        45- |         80        8.88       94.89
        55- |         30        3.33       98.22
        65- |         13        1.44       99.67
        75- |          3        0.33      100.00
------------+-----------------------------------
      Total |        901      100.00
Click to Open File:  output/tableA7.doc

. tab agecat if country=="USA"

     agecat |      Freq.     Percent        Cum.
------------+-----------------------------------
         0- |         31        6.65        6.65
        25- |        276       59.23       65.88
        35- |         93       19.96       85.84
        45- |         39        8.37       94.21
        55- |         16        3.43       97.64
        65- |          9        1.93       99.57
        75- |          2        0.43      100.00
------------+-----------------------------------
      Total |        466      100.00

. *Table A8: Method preferences
. asdoc tab methodcat, title(Table A8: Method preferences) replace  save(output/tableA8.doc)

     methodcat |      Freq.     Percent        Cum.
---------------+-----------------------------------
       small N |        160       17.76       17.76
       large N |        341       37.85       55.60
  mixed-method |        400       44.40      100.00
---------------+-----------------------------------
         Total |        901      100.00
Click to Open File:  output/tableA8.doc

. *Table A9: Number of cases
. asdoc tab numberofcases, title(Table A9: Number of cases) replace  save(output/tableA9.doc)

numberofcases_ |
         coded |      Freq.     Percent        Cum.
---------------+-----------------------------------
       small N |        213       23.64       23.64
       large N |        454       50.39       74.03
  mixed-method |        210       23.31       97.34
not applicable |         24        2.66      100.00
---------------+-----------------------------------
         Total |        901      100.00
Click to Open File:  output/tableA9.doc

. *Table A10: Research culture
. asdoc tab culture, title(Table A10: Research culture) replace  save(output/tableA10.doc)

       culture |      Freq.     Percent        Cum.
---------------+-----------------------------------
   Qualitative |        150       16.65       16.65
  Quantitative |        309       34.30       50.94
  Mixed-method |        442       49.06      100.00
---------------+-----------------------------------
         Total |        901      100.00
Click to Open File:  output/tableA10.doc

. *Table A11: Distribution of goals of inference
. asdoc tab vig2outcome, title(Table A11: Distribution of goals of inference) replace  save(output
> /tableA11.doc)

 vig2outcome |      Freq.     Percent        Cum.
-------------+-----------------------------------
 exploratory |        422       46.84       46.84
confirmatory |        221       24.53       71.37
     process |        258       28.63      100.00
-------------+-----------------------------------
       Total |        901      100.00
Click to Open File:  output/tableA11.doc

. 
. *Table A12: Randomization check
. asdoc table1, by(rand2) vars(age contn \ gender cat \ statuscat cat \ culture cat \ continent ca
> t) onecol format(%2.1f) title(Table A12: Randomization check) replace  save(output/tableA12.doc)
  +-------------------------------------------------------------------------------------+
  | Factor                    extensively studied     not extensively studied   p-value |
  |-------------------------------------------------------------------------------------|
  | N                         459                     442                               |
  |-------------------------------------------------------------------------------------|
  | age, mean (SD)            34.674598 (9.3107052)   35.387156 (9.4815474)        0.26 |
  |-------------------------------------------------------------------------------------|
  | gender                                                                         0.60 |
  |    Female                 147 (32.0%)             145 (32.8%)                       |
  |    Male                   294 (64.1%)             285 (64.5%)                       |
  |    Prefer not to answer   18 (3.9%)               12 (2.7%)                         |
  |-------------------------------------------------------------------------------------|
  | statuscat                                                                      0.29 |
  |    Professor              93 (20.3%)              109 (24.7%)                       |
  |    Senior Scholar         150 (32.7%)             136 (30.8%)                       |
  |    Junior Scholar         216 (47.1%)             197 (44.6%)                       |
  |-------------------------------------------------------------------------------------|
  | culture                                                                        0.40 |
  |    Qualitative            70 (15.3%)              80 (18.1%)                        |
  |    Quantitative           165 (35.9%)             144 (32.6%)                       |
  |    Mixed-method           224 (48.8%)             218 (49.3%)                       |
  |-------------------------------------------------------------------------------------|
  | continent                                                                      0.73 |
  |    EU                     219 (47.7%)             216 (48.9%)                       |
  |    US                     240 (52.3%)             226 (51.1%)                       |
  +-------------------------------------------------------------------------------------+
(note: file output/tableA12.doc not found)
Click to Open File:  output/tableA12.doc

. 
. 
. ***Table A13-A15: Regression results
. mlogit vig2outcome i.rand2 i.culture i.gender i.statuscat age i.continent, robust cluster(country)  baseoutcome(1) 

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -783.16904  
Iteration 2:   log pseudolikelihood = -782.05136  
Iteration 3:   log pseudolikelihood = -782.04694  
Iteration 4:   log pseudolikelihood = -782.04694  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -782.04694               Pseudo R2         =     0.1796

                                           (Std. Err. adjusted for 11 clusters in country)
------------------------------------------------------------------------------------------
                         |               Robust
             vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
exploratory              |  (base outcome)
-------------------------+----------------------------------------------------------------
confirmatory             |
                   rand2 |
not extensively studied  |  -2.287053   .1628943   -14.04   0.000     -2.60632   -1.967786
                         |
                 culture |
           Quantitative  |   1.635796   .3610655     4.53   0.000     .9281209    2.343471
           Mixed-method  |   1.217204   .3238476     3.76   0.000     .5824739    1.851933
                         |
                  gender |
                   Male  |   .3066359    .198288     1.55   0.122    -.0820014    .6952733
   Prefer not to answer  |   .1574403   .4869327     0.32   0.746    -.7969303    1.111811
                         |
               statuscat |
         Senior Scholar  |   .2202332   .6167684     0.36   0.721    -.9886107    1.429077
         Junior Scholar  |  -.2231455   .8949445    -0.25   0.803    -1.977205    1.530913
                         |
                     age |  -.0085603   .0263573    -0.32   0.745    -.0602197    .0430992
                         |
               continent |
                     US  |  -.2016951   .1837558    -1.10   0.272    -.5618498    .1584596
                   _cons |  -.4791907   1.626606    -0.29   0.768    -3.667279    2.708898
-------------------------+----------------------------------------------------------------
process                  |
                   rand2 |
not extensively studied  |   -2.56267   .3431025    -7.47   0.000    -3.235138   -1.890201
                         |
                 culture |
           Quantitative  |   -.412419   .2766265    -1.49   0.136     -.954597     .129759
           Mixed-method  |  -.0291203   .2558005    -0.11   0.909      -.53048    .4722395
                         |
                  gender |
                   Male  |   .0240663   .1412343     0.17   0.865    -.2527479    .3008805
   Prefer not to answer  |   .1780907    .571974     0.31   0.756    -.9429577    1.299139
                         |
               statuscat |
         Senior Scholar  |   .5361777   .6323917     0.85   0.397    -.7032872    1.775643
         Junior Scholar  |   .5925152   .7471099     0.79   0.428    -.8717932    2.056824
                         |
                     age |   .0191643   .0221424     0.87   0.387    -.0242341    .0625627
                         |
               continent |
                     US  |   -.383747   .1449531    -2.65   0.008    -.6678498   -.0996442
                   _cons |  -.0382924   1.531226    -0.03   0.980     -3.03944    2.962855
------------------------------------------------------------------------------------------

. eststo
(est1 stored)

. esttab using "output/tableA13.rtf", unstack noomitted label compress replace  onecell se b(%10.2f) title(Multinomial logit models with base outcome "exploratory")  star(* 0.05)
(output written to output/tableA13.rtf)

. est clear

. mlogit vig2outcome i.rand2 i.culture i.gender i.statuscat age i.continent, robust cluster(country)  baseoutcome(2)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -783.16904  
Iteration 2:   log pseudolikelihood = -782.05136  
Iteration 3:   log pseudolikelihood = -782.04694  
Iteration 4:   log pseudolikelihood = -782.04694  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -782.04694               Pseudo R2         =     0.1796

                                           (Std. Err. adjusted for 11 clusters in country)
------------------------------------------------------------------------------------------
                         |               Robust
             vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
exploratory              |
                   rand2 |
not extensively studied  |   2.287053   .1628943    14.04   0.000     1.967786     2.60632
                         |
                 culture |
           Quantitative  |  -1.635796   .3610655    -4.53   0.000    -2.343471   -.9281209
           Mixed-method  |  -1.217204   .3238476    -3.76   0.000    -1.851933   -.5824739
                         |
                  gender |
                   Male  |  -.3066359    .198288    -1.55   0.122    -.6952733    .0820014
   Prefer not to answer  |  -.1574403   .4869327    -0.32   0.746    -1.111811    .7969303
                         |
               statuscat |
         Senior Scholar  |  -.2202332   .6167684    -0.36   0.721    -1.429077    .9886107
         Junior Scholar  |   .2231455   .8949445     0.25   0.803    -1.530913    1.977205
                         |
                     age |   .0085603   .0263573     0.32   0.745    -.0430992    .0602197
                         |
               continent |
                     US  |   .2016951   .1837558     1.10   0.272    -.1584596    .5618498
                   _cons |   .4791907   1.626606     0.29   0.768    -2.708898    3.667279
-------------------------+----------------------------------------------------------------
confirmatory             |  (base outcome)
-------------------------+----------------------------------------------------------------
process                  |
                   rand2 |
not extensively studied  |  -.2756164     .28999    -0.95   0.342    -.8439865    .2927536
                         |
                 culture |
           Quantitative  |  -2.048215   .3982136    -5.14   0.000    -2.828699   -1.267731
           Mixed-method  |  -1.246324   .4022581    -3.10   0.002    -2.034735   -.4579125
                         |
                  gender |
                   Male  |  -.2825696   .2275627    -1.24   0.214    -.7285844    .1634452
   Prefer not to answer  |   .0206504   .3711843     0.06   0.956    -.7068574    .7481582
                         |
               statuscat |
         Senior Scholar  |   .3159445   .3632696     0.87   0.384    -.3960508     1.02794
         Junior Scholar  |   .8156607   .5949545     1.37   0.170    -.3504286     1.98175
                         |
                     age |   .0277246   .0181112     1.53   0.126    -.0077728     .063222
                         |
               continent |
                     US  |  -.1820519   .1275329    -1.43   0.153    -.4320117    .0679079
                   _cons |   .4408983   1.251097     0.35   0.725    -2.011207    2.893004
------------------------------------------------------------------------------------------

. eststo
(est1 stored)

. esttab using "output/tableA14.rtf", unstack noomitted label compress replace  onecell se b(%10.2f) title(Multinomial logit models with base outcome "confirmatory")  star(* 0.05)
(output written to output/tableA14.rtf)

. est clear

. mlogit vig2outcome i.rand2 i.culture i.gender i.statuscat age i.continent, robust cluster(country)  baseoutcome(3)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -783.16904  
Iteration 2:   log pseudolikelihood = -782.05136  
Iteration 3:   log pseudolikelihood = -782.04694  
Iteration 4:   log pseudolikelihood = -782.04694  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -782.04694               Pseudo R2         =     0.1796

                                           (Std. Err. adjusted for 11 clusters in country)
------------------------------------------------------------------------------------------
                         |               Robust
             vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
exploratory              |
                   rand2 |
not extensively studied  |    2.56267   .3431025     7.47   0.000     1.890201    3.235138
                         |
                 culture |
           Quantitative  |    .412419   .2766265     1.49   0.136     -.129759     .954597
           Mixed-method  |   .0291203   .2558005     0.11   0.909    -.4722395      .53048
                         |
                  gender |
                   Male  |  -.0240663   .1412343    -0.17   0.865    -.3008805    .2527479
   Prefer not to answer  |  -.1780907    .571974    -0.31   0.756    -1.299139    .9429577
                         |
               statuscat |
         Senior Scholar  |  -.5361777   .6323917    -0.85   0.397    -1.775643    .7032872
         Junior Scholar  |  -.5925152   .7471099    -0.79   0.428    -2.056824    .8717932
                         |
                     age |  -.0191643   .0221424    -0.87   0.387    -.0625627    .0242341
                         |
               continent |
                     US  |    .383747   .1449531     2.65   0.008     .0996442    .6678498
                   _cons |   .0382924   1.531226     0.03   0.980    -2.962855     3.03944
-------------------------+----------------------------------------------------------------
confirmatory             |
                   rand2 |
not extensively studied  |   .2756164     .28999     0.95   0.342    -.2927536    .8439865
                         |
                 culture |
           Quantitative  |   2.048215   .3982136     5.14   0.000     1.267731    2.828699
           Mixed-method  |   1.246324   .4022581     3.10   0.002     .4579125    2.034735
                         |
                  gender |
                   Male  |   .2825696   .2275627     1.24   0.214    -.1634452    .7285844
   Prefer not to answer  |  -.0206504   .3711843    -0.06   0.956    -.7481582    .7068574
                         |
               statuscat |
         Senior Scholar  |  -.3159445   .3632696    -0.87   0.384     -1.02794    .3960508
         Junior Scholar  |  -.8156607   .5949545    -1.37   0.170     -1.98175    .3504286
                         |
                     age |  -.0277246   .0181112    -1.53   0.126     -.063222    .0077728
                         |
               continent |
                     US  |   .1820519   .1275329     1.43   0.153    -.0679079    .4320117
                   _cons |  -.4408983   1.251097    -0.35   0.725    -2.893004    2.011207
-------------------------+----------------------------------------------------------------
process                  |  (base outcome)
------------------------------------------------------------------------------------------

. eststo
(est1 stored)

. esttab using "output/tableA15.rtf", unstack noomitted label compress replace  onecell se b(%10.2f) title(Multinomial logit models with base outcome "process")  star(* 0.05)
(output written to output/tableA15.rtf)

. est clear

. 
. 
. *** Interaction effects
. *Figure A2: Treatment effect accross research cultures
. mlogit vig2outcome i.culture#i.rand2 i.gender i.statuscat age i.continent, robust cluster(country)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -787.26049  
Iteration 2:   log pseudolikelihood = -780.42323  
Iteration 3:   log pseudolikelihood = -780.20731  
Iteration 4:   log pseudolikelihood = -780.20627  
Iteration 5:   log pseudolikelihood = -780.20627  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -780.20627               Pseudo R2         =     0.1816

                                                        (Std. Err. adjusted for 11 clusters in country)
-------------------------------------------------------------------------------------------------------
                                      |               Robust
                          vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------------------------+----------------------------------------------------------------
exploratory                           |  (base outcome)
--------------------------------------+----------------------------------------------------------------
confirmatory                          |
                        culture#rand2 |
 Qualitative#not extensively studied  |  -2.841242   1.201989    -2.36   0.018    -5.197096   -.4853878
    Quantitative#extensively studied  |   1.438657   .5641098     2.55   0.011     .3330219    2.544292
Quantitative#not extensively studied  |  -.5679765   .6043254    -0.94   0.347    -1.752433    .6164796
    Mixed-method#extensively studied  |   1.234054   .6766894     1.82   0.068    -.0922327    2.560341
Mixed-method#not extensively studied  |  -1.296417   .5452041    -2.38   0.017    -2.364997   -.2278365
                                      |
                               gender |
                                Male  |   .3180979   .1968317     1.62   0.106     -.067685    .7038809
                Prefer not to answer  |   .2097612   .4908666     0.43   0.669    -.7523195    1.171842
                                      |
                            statuscat |
                      Senior Scholar  |   .2366736   .6241525     0.38   0.705    -.9866428     1.45999
                      Junior Scholar  |  -.1950833   .8894799    -0.22   0.826    -1.938432    1.548265
                                      |
                                  age |  -.0075163   .0264733    -0.28   0.776     -.059403    .0443704
                                      |
                            continent |
                                  US  |  -.1971909   .1822695    -1.08   0.279    -.5544327    .1600508
                                _cons |  -.4593042   1.894858    -0.24   0.808    -4.173158     3.25455
--------------------------------------+----------------------------------------------------------------
process                               |
                        culture#rand2 |
 Qualitative#not extensively studied  |  -2.313064   .3881075    -5.96   0.000    -3.073741   -1.552388
    Quantitative#extensively studied  |  -.3175673   .4994801    -0.64   0.525     -1.29653    .6613957
Quantitative#not extensively studied  |  -3.096253   .4776744    -6.48   0.000    -4.032478   -2.160028
    Mixed-method#extensively studied  |   .1222677   .5027217     0.24   0.808    -.8630487    1.107584
Mixed-method#not extensively studied  |  -2.490205   .4846764    -5.14   0.000    -3.440153   -1.540256
                                      |
                               gender |
                                Male  |   .0279149   .1386191     0.20   0.840    -.2437735    .2996032
                Prefer not to answer  |   .1841064   .5605129     0.33   0.743    -.9144787    1.282691
                                      |
                            statuscat |
                      Senior Scholar  |   .5284773   .6191268     0.85   0.393     -.684989    1.741944
                      Junior Scholar  |   .5709383    .718145     0.80   0.427    -.8366001    1.978477
                                      |
                                  age |   .0183435     .02098     0.87   0.382    -.0227765    .0594636
                                      |
                            continent |
                                  US  |  -.3860685   .1443396    -2.67   0.007     -.668969    -.103168
                                _cons |  -.0957295   1.655041    -0.06   0.954     -3.33955    3.148091
-------------------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2) at(culture=(1 2 3)) pr(out(1)) post

Average marginal effects                        Number of obs     =        901
Model VCE    : Robust

Expression   : Pr(vig2outcome==exploratory), predict(out(1))
dy/dx w.r.t. : 2.rand2

1._at        : culture         =           1

2._at        : culture         =           2

3._at        : culture         =           3

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.rand2      |  (base outcome)
-------------+----------------------------------------------------------------
2.rand2      |
         _at |
          1  |   .5281512   .0870222     6.07   0.000     .3575908    .6987117
          2  |     .50245    .048629    10.33   0.000     .4071389    .5977612
          3  |   .5594833   .0554149    10.10   0.000     .4508721    .6680946
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, xline(0, lpattern(dash) lcolor(black)) title(Exploratory, size(small)) coeflabels(1._at = "Qualitative" 2._at = "Quantitative"  3._at = "Mixed-Methods") xscale(range(-0.7 0.7)) xlabel(-0.7
> (0.1)0.7) nodraw mcolor(black) mfcolor(black) msymbol(diamond)  ciopts(lcolor(black))

. graph save Graph "output/figA2m1.gph", replace
(file output/figA2m1.gph saved)

. est clear

. mlogit vig2outcome i.culture#i.rand2 i.gender i.statuscat age i.continent, robust cluster(country)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -787.26049  
Iteration 2:   log pseudolikelihood = -780.42323  
Iteration 3:   log pseudolikelihood = -780.20731  
Iteration 4:   log pseudolikelihood = -780.20627  
Iteration 5:   log pseudolikelihood = -780.20627  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -780.20627               Pseudo R2         =     0.1816

                                                        (Std. Err. adjusted for 11 clusters in country)
-------------------------------------------------------------------------------------------------------
                                      |               Robust
                          vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------------------------+----------------------------------------------------------------
exploratory                           |  (base outcome)
--------------------------------------+----------------------------------------------------------------
confirmatory                          |
                        culture#rand2 |
 Qualitative#not extensively studied  |  -2.841242   1.201989    -2.36   0.018    -5.197096   -.4853878
    Quantitative#extensively studied  |   1.438657   .5641098     2.55   0.011     .3330219    2.544292
Quantitative#not extensively studied  |  -.5679765   .6043254    -0.94   0.347    -1.752433    .6164796
    Mixed-method#extensively studied  |   1.234054   .6766894     1.82   0.068    -.0922327    2.560341
Mixed-method#not extensively studied  |  -1.296417   .5452041    -2.38   0.017    -2.364997   -.2278365
                                      |
                               gender |
                                Male  |   .3180979   .1968317     1.62   0.106     -.067685    .7038809
                Prefer not to answer  |   .2097612   .4908666     0.43   0.669    -.7523195    1.171842
                                      |
                            statuscat |
                      Senior Scholar  |   .2366736   .6241525     0.38   0.705    -.9866428     1.45999
                      Junior Scholar  |  -.1950833   .8894799    -0.22   0.826    -1.938432    1.548265
                                      |
                                  age |  -.0075163   .0264733    -0.28   0.776     -.059403    .0443704
                                      |
                            continent |
                                  US  |  -.1971909   .1822695    -1.08   0.279    -.5544327    .1600508
                                _cons |  -.4593042   1.894858    -0.24   0.808    -4.173158     3.25455
--------------------------------------+----------------------------------------------------------------
process                               |
                        culture#rand2 |
 Qualitative#not extensively studied  |  -2.313064   .3881075    -5.96   0.000    -3.073741   -1.552388
    Quantitative#extensively studied  |  -.3175673   .4994801    -0.64   0.525     -1.29653    .6613957
Quantitative#not extensively studied  |  -3.096253   .4776744    -6.48   0.000    -4.032478   -2.160028
    Mixed-method#extensively studied  |   .1222677   .5027217     0.24   0.808    -.8630487    1.107584
Mixed-method#not extensively studied  |  -2.490205   .4846764    -5.14   0.000    -3.440153   -1.540256
                                      |
                               gender |
                                Male  |   .0279149   .1386191     0.20   0.840    -.2437735    .2996032
                Prefer not to answer  |   .1841064   .5605129     0.33   0.743    -.9144787    1.282691
                                      |
                            statuscat |
                      Senior Scholar  |   .5284773   .6191268     0.85   0.393     -.684989    1.741944
                      Junior Scholar  |   .5709383    .718145     0.80   0.427    -.8366001    1.978477
                                      |
                                  age |   .0183435     .02098     0.87   0.382    -.0227765    .0594636
                                      |
                            continent |
                                  US  |  -.3860685   .1443396    -2.67   0.007     -.668969    -.103168
                                _cons |  -.0957295   1.655041    -0.06   0.954     -3.33955    3.148091
-------------------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2) at(culture=(1 2 3)) pr(out(2)) post

Average marginal effects                        Number of obs     =        901
Model VCE    : Robust

Expression   : Pr(vig2outcome==confirmatory), predict(out(2))
dy/dx w.r.t. : 2.rand2

1._at        : culture         =           1

2._at        : culture         =           2

3._at        : culture         =           3

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.rand2      |  (base outcome)
-------------+----------------------------------------------------------------
2.rand2      |
         _at |
          1  |  -.1180381   .0632309    -1.87   0.062    -.2419685    .0058922
          2  |   -.242328   .0491351    -4.93   0.000    -.3386311   -.1460249
          3  |  -.2315818   .0305835    -7.57   0.000    -.2915243   -.1716392
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, xline(0, lpattern(dash) lcolor(black)) title(Confirmatory, size(small)) coeflabels(1._at = "Qualitative" 2._at = "Quantitative"  3._at = "Mixed-Methods") xscale(range(-0.7 0.7)) xlabel(-0.
> 7(0.1)0.7) nodraw mcolor(black) mfcolor(black) msymbol(diamond)  ciopts(lcolor(black))

. graph save Graph "output/figA2m2.gph", replace
(file output/figA2m2.gph saved)

. est clear

. mlogit vig2outcome i.culture#i.rand2 i.gender i.statuscat age i.continent, robust cluster(country)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -787.26049  
Iteration 2:   log pseudolikelihood = -780.42323  
Iteration 3:   log pseudolikelihood = -780.20731  
Iteration 4:   log pseudolikelihood = -780.20627  
Iteration 5:   log pseudolikelihood = -780.20627  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -780.20627               Pseudo R2         =     0.1816

                                                        (Std. Err. adjusted for 11 clusters in country)
-------------------------------------------------------------------------------------------------------
                                      |               Robust
                          vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------------------------+----------------------------------------------------------------
exploratory                           |  (base outcome)
--------------------------------------+----------------------------------------------------------------
confirmatory                          |
                        culture#rand2 |
 Qualitative#not extensively studied  |  -2.841242   1.201989    -2.36   0.018    -5.197096   -.4853878
    Quantitative#extensively studied  |   1.438657   .5641098     2.55   0.011     .3330219    2.544292
Quantitative#not extensively studied  |  -.5679765   .6043254    -0.94   0.347    -1.752433    .6164796
    Mixed-method#extensively studied  |   1.234054   .6766894     1.82   0.068    -.0922327    2.560341
Mixed-method#not extensively studied  |  -1.296417   .5452041    -2.38   0.017    -2.364997   -.2278365
                                      |
                               gender |
                                Male  |   .3180979   .1968317     1.62   0.106     -.067685    .7038809
                Prefer not to answer  |   .2097612   .4908666     0.43   0.669    -.7523195    1.171842
                                      |
                            statuscat |
                      Senior Scholar  |   .2366736   .6241525     0.38   0.705    -.9866428     1.45999
                      Junior Scholar  |  -.1950833   .8894799    -0.22   0.826    -1.938432    1.548265
                                      |
                                  age |  -.0075163   .0264733    -0.28   0.776     -.059403    .0443704
                                      |
                            continent |
                                  US  |  -.1971909   .1822695    -1.08   0.279    -.5544327    .1600508
                                _cons |  -.4593042   1.894858    -0.24   0.808    -4.173158     3.25455
--------------------------------------+----------------------------------------------------------------
process                               |
                        culture#rand2 |
 Qualitative#not extensively studied  |  -2.313064   .3881075    -5.96   0.000    -3.073741   -1.552388
    Quantitative#extensively studied  |  -.3175673   .4994801    -0.64   0.525     -1.29653    .6613957
Quantitative#not extensively studied  |  -3.096253   .4776744    -6.48   0.000    -4.032478   -2.160028
    Mixed-method#extensively studied  |   .1222677   .5027217     0.24   0.808    -.8630487    1.107584
Mixed-method#not extensively studied  |  -2.490205   .4846764    -5.14   0.000    -3.440153   -1.540256
                                      |
                               gender |
                                Male  |   .0279149   .1386191     0.20   0.840    -.2437735    .2996032
                Prefer not to answer  |   .1841064   .5605129     0.33   0.743    -.9144787    1.282691
                                      |
                            statuscat |
                      Senior Scholar  |   .5284773   .6191268     0.85   0.393     -.684989    1.741944
                      Junior Scholar  |   .5709383    .718145     0.80   0.427    -.8366001    1.978477
                                      |
                                  age |   .0183435     .02098     0.87   0.382    -.0227765    .0594636
                                      |
                            continent |
                                  US  |  -.3860685   .1443396    -2.67   0.007     -.668969    -.103168
                                _cons |  -.0957295   1.655041    -0.06   0.954     -3.33955    3.148091
-------------------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2) at(culture=(1 2 3)) pr(out(3)) post

Average marginal effects                        Number of obs     =        901
Model VCE    : Robust

Expression   : Pr(vig2outcome==process), predict(out(3))
dy/dx w.r.t. : 2.rand2

1._at        : culture         =           1

2._at        : culture         =           2

3._at        : culture         =           3

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.rand2      |  (base outcome)
-------------+----------------------------------------------------------------
2.rand2      |
         _at |
          1  |  -.4101131   .0824755    -4.97   0.000     -.571762   -.2484642
          2  |   -.260122   .0397833    -6.54   0.000    -.3380958   -.1821482
          3  |  -.3279016   .0556478    -5.89   0.000    -.4369692   -.2188339
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, xline(0, lpattern(dash) lcolor(black)) title(Process, size(small)) coeflabels(1._at = "Qualitative" 2._at = "Quantitative"  3._at = "Mixed-Methods") xscale(range(-0.7 0.7)) xlabel(-0.7(0.1
> )0.7) nodraw mcolor(black) mfcolor(black) msymbol(diamond)  ciopts(lcolor(black))

. graph save Graph "output/figA2m3.gph", replace
(file output/figA2m3.gph saved)

. est clear

. graph combine "output/figA2m1.gph" "output/figA2m2.gph" "output/figA2m3.gph", rows(3) imargin(small) title("") commonscheme graphregion(color(white) margin(small) fcolor(white)) ycommon

. graph save Graph "output/figA2.gph", replace
(file output/figA2.gph saved)

. graph export "output/figureA2.tif", width(1000) replace
(file output/figureA2.tif written in TIFF format)

. 
. *Figure A3:Probability of choosing a specific goal of inference across research cultures and treatment conditions
. mlogit vig2outcome i.culture#i.rand2 i.gender i.statuscat age i.continent, robust cluster(country)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -787.26049  
Iteration 2:   log pseudolikelihood = -780.42323  
Iteration 3:   log pseudolikelihood = -780.20731  
Iteration 4:   log pseudolikelihood = -780.20627  
Iteration 5:   log pseudolikelihood = -780.20627  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -780.20627               Pseudo R2         =     0.1816

                                                        (Std. Err. adjusted for 11 clusters in country)
-------------------------------------------------------------------------------------------------------
                                      |               Robust
                          vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------------------------+----------------------------------------------------------------
exploratory                           |  (base outcome)
--------------------------------------+----------------------------------------------------------------
confirmatory                          |
                        culture#rand2 |
 Qualitative#not extensively studied  |  -2.841242   1.201989    -2.36   0.018    -5.197096   -.4853878
    Quantitative#extensively studied  |   1.438657   .5641098     2.55   0.011     .3330219    2.544292
Quantitative#not extensively studied  |  -.5679765   .6043254    -0.94   0.347    -1.752433    .6164796
    Mixed-method#extensively studied  |   1.234054   .6766894     1.82   0.068    -.0922327    2.560341
Mixed-method#not extensively studied  |  -1.296417   .5452041    -2.38   0.017    -2.364997   -.2278365
                                      |
                               gender |
                                Male  |   .3180979   .1968317     1.62   0.106     -.067685    .7038809
                Prefer not to answer  |   .2097612   .4908666     0.43   0.669    -.7523195    1.171842
                                      |
                            statuscat |
                      Senior Scholar  |   .2366736   .6241525     0.38   0.705    -.9866428     1.45999
                      Junior Scholar  |  -.1950833   .8894799    -0.22   0.826    -1.938432    1.548265
                                      |
                                  age |  -.0075163   .0264733    -0.28   0.776     -.059403    .0443704
                                      |
                            continent |
                                  US  |  -.1971909   .1822695    -1.08   0.279    -.5544327    .1600508
                                _cons |  -.4593042   1.894858    -0.24   0.808    -4.173158     3.25455
--------------------------------------+----------------------------------------------------------------
process                               |
                        culture#rand2 |
 Qualitative#not extensively studied  |  -2.313064   .3881075    -5.96   0.000    -3.073741   -1.552388
    Quantitative#extensively studied  |  -.3175673   .4994801    -0.64   0.525     -1.29653    .6613957
Quantitative#not extensively studied  |  -3.096253   .4776744    -6.48   0.000    -4.032478   -2.160028
    Mixed-method#extensively studied  |   .1222677   .5027217     0.24   0.808    -.8630487    1.107584
Mixed-method#not extensively studied  |  -2.490205   .4846764    -5.14   0.000    -3.440153   -1.540256
                                      |
                               gender |
                                Male  |   .0279149   .1386191     0.20   0.840    -.2437735    .2996032
                Prefer not to answer  |   .1841064   .5605129     0.33   0.743    -.9144787    1.282691
                                      |
                            statuscat |
                      Senior Scholar  |   .5284773   .6191268     0.85   0.393     -.684989    1.741944
                      Junior Scholar  |   .5709383    .718145     0.80   0.427    -.8366001    1.978477
                                      |
                                  age |   .0183435     .02098     0.87   0.382    -.0227765    .0594636
                                      |
                            continent |
                                  US  |  -.3860685   .1443396    -2.67   0.007     -.668969    -.103168
                                _cons |  -.0957295   1.655041    -0.06   0.954     -3.33955    3.148091
-------------------------------------------------------------------------------------------------------

. est store model

. 
. margins if rand2 == 1, at(culture=(1 2 3) rand=(1)) post pr(out(1))

Predictive margins                              Number of obs     =        459
Model VCE    : Robust

Expression   : Pr(vig2outcome==exploratory), predict(out(1))

1._at        : culture         =           1
               rand2           =           1

2._at        : culture         =           2
               rand2           =           1

3._at        : culture         =           3
               rand2           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2665041   .0869445     3.07   0.002     .0960959    .4369122
          2  |   .2058243   .0307138     6.70   0.000     .1456264    .2660222
          3  |   .1874201   .0278759     6.72   0.000     .1327843     .242056
------------------------------------------------------------------------------

. est store ext

. est restore model
(results model are active now)

. margins if rand2 == 2, at(culture=(1 2 3) rand2=(2)) post pr(out(1))

Predictive margins                              Number of obs     =        442
Model VCE    : Robust

Expression   : Pr(vig2outcome==exploratory), predict(out(1))

1._at        : culture         =           1
               rand2           =           2

2._at        : culture         =           2
               rand2           =           2

3._at        : culture         =           3
               rand2           =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .7960584   .0196182    40.58   0.000     .7576074    .8345094
          2  |   .7093684   .0374284    18.95   0.000     .6360101    .7827267
          3  |   .7480732   .0307869    24.30   0.000     .6877319    .8084145
------------------------------------------------------------------------------

. est store notext

. 
. coefplot        (ext, msymbol(diamond) msize(small) col(black) ciopt(col(black))) ///
>                         (notext, msize(medium) col(gs8) ciopt(col(gs8))), ///
>         legend(off) ///
>         xlab(0(.1).9, format(%2.1f)) xtitle("Pr(Exploratory)", size(small)) ///
>         ylab(1 `" "Qualitative" "' 2 `" "Quantitative" "' ///
>                 3 `" "Mixed-Method" "', labsize(small)) grid(none)

. graph save Graph "output/figA3m1.gph", replace
(file output/figA3m1.gph saved)

. 
. est restore model
(results model are active now)

. margins if rand2 == 1, at(culture=(1 2 3) rand=(1)) post pr(out(2))

Predictive margins                              Number of obs     =        459
Model VCE    : Robust

Expression   : Pr(vig2outcome==confirmatory), predict(out(2))

1._at        : culture         =           1
               rand2           =           1

2._at        : culture         =           2
               rand2           =           1

3._at        : culture         =           3
               rand2           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1435294   .0535521     2.68   0.007     .0385693    .2484896
          2  |   .4605699   .0351993    13.08   0.000     .3915805    .5295592
          3  |   .3431858   .0281055    12.21   0.000     .2880999    .3982716
------------------------------------------------------------------------------

. est store ext

. est restore model
(results model are active now)

. margins if rand2 == 2, at(culture=(1 2 3) rand2=(2)) post pr(out(2))

Predictive margins                              Number of obs     =        442
Model VCE    : Robust

Expression   : Pr(vig2outcome==confirmatory), predict(out(2))

1._at        : culture         =           1
               rand2           =           2

2._at        : culture         =           2
               rand2           =           2

3._at        : culture         =           3
               rand2           =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0254313   .0176968     1.44   0.151    -.0092539    .0601164
          2  |   .2179217   .0341601     6.38   0.000     .1509692    .2848742
          3  |   .1114848   .0114354     9.75   0.000     .0890718    .1338978
------------------------------------------------------------------------------

. est store notext

. 
. coefplot        (ext, msymbol(diamond) msize(small) col(black) ciopt(col(black))) ///
>                         (notext, msize(medium) col(gs8) ciopt(col(gs8))), ///
>         legend(off) ///
>         xlab(0(.1).9, format(%2.1f)) xtitle("Pr(Confirmatory)", size(small)) ///
>         ylab(1 `" "Qualitative" "' 2 `" "Quantitative" "' ///
>                 3 `" "Mixed-Method" "', labsize(small)) grid(none)

. graph save Graph "output/figA3m2.gph", replace
(file output/figA3m2.gph saved)

. 
. est restore model
(results model are active now)

. margins if rand2 == 1, at(culture=(1 2 3) rand=(1)) post pr(out(3))

Predictive margins                              Number of obs     =        459
Model VCE    : Robust

Expression   : Pr(vig2outcome==process), predict(out(3))

1._at        : culture         =           1
               rand2           =           1

2._at        : culture         =           2
               rand2           =           1

3._at        : culture         =           3
               rand2           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .5899665   .0904236     6.52   0.000     .4127396    .7671934
          2  |   .3336059   .0271539    12.29   0.000     .2803852    .3868265
          3  |   .4693941   .0366476    12.81   0.000     .3975661    .5412221
------------------------------------------------------------------------------

. est store ext

. est restore model
(results model are active now)

. margins if rand2 == 2, at(culture=(1 2 3) rand2=(2)) post pr(out(3))

Predictive margins                              Number of obs     =        442
Model VCE    : Robust

Expression   : Pr(vig2outcome==process), predict(out(3))

1._at        : culture         =           1
               rand2           =           2

2._at        : culture         =           2
               rand2           =           2

3._at        : culture         =           3
               rand2           =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1785103   .0271384     6.58   0.000       .12532    .2317006
          2  |   .0727099   .0197519     3.68   0.000     .0339969    .1114229
          3  |    .140442   .0293908     4.78   0.000     .0828371    .1980469
------------------------------------------------------------------------------

. est store notext

. 
. coefplot        (ext, msymbol(diamond) msize(small) col(black) ciopt(col(black))) ///
>                         (notext, msize(medium) col(gs8) ciopt(col(gs8))), ///
>         legend(order(2 "extensively studied" 4 "not extensively studied") position(6) cols(2)) ///
>         xlab(0(.1).9, format(%2.1f)) xtitle("Pr(Process)", size(small)) ///
>         ylab(1 `" "Qualitative" "' 2 `" "Quantitative" "' ///
>                 3 `" "Mixed-Method" "', labsize(small)) grid(none)

. graph save Graph "output/figA3m3.gph", replace
(file output/figA3m3.gph saved)

. 
. graph combine "output/figA3m1.gph" "output/figA3m2.gph" "output/figA3m3.gph", rows(3) imargin(small) title("") commonscheme graphregion(color(white) margin(small) fcolor(white)) xcommon ycommon

. graph save Graph "output/figA3.gph", replace
(file output/figA3.gph saved)

. graph export "output/figureA3.tif", width(1000) replace
(file output/figureA3.tif written in TIFF format)

. 
. ***Wald tests of differences between combinations of treatment and research methods
. est restore model
(results model are active now)

. margins, over(rand2) at(culture=(1 2 3)) post

Predictive margins                              Number of obs     =        901
Model VCE    : Robust

over         : rand2
1._predict   : Pr(vig2outcome==exploratory), predict(pr outcome(1))
2._predict   : Pr(vig2outcome==confirmatory), predict(pr outcome(2))
3._predict   : Pr(vig2outcome==process), predict(pr outcome(3))

1._at        : 1.rand2
                   culture         =           1
               2.rand2
                   culture         =           1

2._at        : 1.rand2
                   culture         =           2
               2.rand2
                   culture         =           2

3._at        : 1.rand2
                   culture         =           3
               2.rand2
                   culture         =           3

----------------------------------------------------------------------------------------------
                             |            Delta-method
                             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
          _predict#_at#rand2 |
    1#1#extensively studied  |   .2665041   .0869445     3.07   0.002     .0960959    .4369122
1#1#not extensively studied  |   .7960584   .0196182    40.58   0.000     .7576074    .8345094
    1#2#extensively studied  |   .2058243   .0307138     6.70   0.000     .1456264    .2660222
1#2#not extensively studied  |   .7093684   .0374284    18.95   0.000     .6360101    .7827267
    1#3#extensively studied  |   .1874201   .0278759     6.72   0.000     .1327843     .242056
1#3#not extensively studied  |   .7480732   .0307869    24.30   0.000     .6877319    .8084145
    2#1#extensively studied  |   .1435294   .0535521     2.68   0.007     .0385693    .2484896
2#1#not extensively studied  |   .0254313   .0176968     1.44   0.151    -.0092539    .0601164
    2#2#extensively studied  |   .4605699   .0351993    13.08   0.000     .3915805    .5295592
2#2#not extensively studied  |   .2179217   .0341601     6.38   0.000     .1509692    .2848742
    2#3#extensively studied  |   .3431858   .0281055    12.21   0.000     .2880999    .3982716
2#3#not extensively studied  |   .1114848   .0114354     9.75   0.000     .0890718    .1338978
    3#1#extensively studied  |   .5899665   .0904236     6.52   0.000     .4127396    .7671934
3#1#not extensively studied  |   .1785103   .0271384     6.58   0.000       .12532    .2317006
    3#2#extensively studied  |   .3336059   .0271539    12.29   0.000     .2803852    .3868265
3#2#not extensively studied  |   .0727099   .0197519     3.68   0.000     .0339969    .1114229
    3#3#extensively studied  |   .4693941   .0366476    12.81   0.000     .3975661    .5412221
3#3#not extensively studied  |    .140442   .0293908     4.78   0.000     .0828371    .1980469
----------------------------------------------------------------------------------------------

. *Difference between quantitative and mixed method researchers in choosing a confirmatory outcome given an extensively studied research topic
. mlincom 9-11

             |   lincom    pvalue        ll        ul 
-------------+----------------------------------------
           1 |    0.117     0.039     0.006     0.229 

. 
. *Reproduction of figure A3 with alternative logit transformation for standard errors to avoid lower bound CIs below zero
. mlogit vig2outcome i.culture#i.rand2 i.gender i.statuscat age i.continent, robust cluster(country)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -787.26049  
Iteration 2:   log pseudolikelihood = -780.42323  
Iteration 3:   log pseudolikelihood = -780.20731  
Iteration 4:   log pseudolikelihood = -780.20627  
Iteration 5:   log pseudolikelihood = -780.20627  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -780.20627               Pseudo R2         =     0.1816

                                                        (Std. Err. adjusted for 11 clusters in country)
-------------------------------------------------------------------------------------------------------
                                      |               Robust
                          vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------------------------+----------------------------------------------------------------
exploratory                           |  (base outcome)
--------------------------------------+----------------------------------------------------------------
confirmatory                          |
                        culture#rand2 |
 Qualitative#not extensively studied  |  -2.841242   1.201989    -2.36   0.018    -5.197096   -.4853878
    Quantitative#extensively studied  |   1.438657   .5641098     2.55   0.011     .3330219    2.544292
Quantitative#not extensively studied  |  -.5679765   .6043254    -0.94   0.347    -1.752433    .6164796
    Mixed-method#extensively studied  |   1.234054   .6766894     1.82   0.068    -.0922327    2.560341
Mixed-method#not extensively studied  |  -1.296417   .5452041    -2.38   0.017    -2.364997   -.2278365
                                      |
                               gender |
                                Male  |   .3180979   .1968317     1.62   0.106     -.067685    .7038809
                Prefer not to answer  |   .2097612   .4908666     0.43   0.669    -.7523195    1.171842
                                      |
                            statuscat |
                      Senior Scholar  |   .2366736   .6241525     0.38   0.705    -.9866428     1.45999
                      Junior Scholar  |  -.1950833   .8894799    -0.22   0.826    -1.938432    1.548265
                                      |
                                  age |  -.0075163   .0264733    -0.28   0.776     -.059403    .0443704
                                      |
                            continent |
                                  US  |  -.1971909   .1822695    -1.08   0.279    -.5544327    .1600508
                                _cons |  -.4593042   1.894858    -0.24   0.808    -4.173158     3.25455
--------------------------------------+----------------------------------------------------------------
process                               |
                        culture#rand2 |
 Qualitative#not extensively studied  |  -2.313064   .3881075    -5.96   0.000    -3.073741   -1.552388
    Quantitative#extensively studied  |  -.3175673   .4994801    -0.64   0.525     -1.29653    .6613957
Quantitative#not extensively studied  |  -3.096253   .4776744    -6.48   0.000    -4.032478   -2.160028
    Mixed-method#extensively studied  |   .1222677   .5027217     0.24   0.808    -.8630487    1.107584
Mixed-method#not extensively studied  |  -2.490205   .4846764    -5.14   0.000    -3.440153   -1.540256
                                      |
                               gender |
                                Male  |   .0279149   .1386191     0.20   0.840    -.2437735    .2996032
                Prefer not to answer  |   .1841064   .5605129     0.33   0.743    -.9144787    1.282691
                                      |
                            statuscat |
                      Senior Scholar  |   .5284773   .6191268     0.85   0.393     -.684989    1.741944
                      Junior Scholar  |   .5709383    .718145     0.80   0.427    -.8366001    1.978477
                                      |
                                  age |   .0183435     .02098     0.87   0.382    -.0227765    .0594636
                                      |
                            continent |
                                  US  |  -.3860685   .1443396    -2.67   0.007     -.668969    -.103168
                                _cons |  -.0957295   1.655041    -0.06   0.954     -3.33955    3.148091
-------------------------------------------------------------------------------------------------------

. est store model

. 
. margins if rand2 == 1, at(culture=(1 2 3) rand=(1)) post pr(out(1))

Predictive margins                              Number of obs     =        459
Model VCE    : Robust

Expression   : Pr(vig2outcome==exploratory), predict(out(1))

1._at        : culture         =           1
               rand2           =           1

2._at        : culture         =           2
               rand2           =           1

3._at        : culture         =           3
               rand2           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2665041   .0869445     3.07   0.002     .0960959    .4369122
          2  |   .2058243   .0307138     6.70   0.000     .1456264    .2660222
          3  |   .1874201   .0278759     6.72   0.000     .1327843     .242056
------------------------------------------------------------------------------

. est store ext

. est restore model
(results model are active now)

. margins if rand2 == 2, at(culture=(1 2 3) rand2=(2)) post pr(out(1))

Predictive margins                              Number of obs     =        442
Model VCE    : Robust

Expression   : Pr(vig2outcome==exploratory), predict(out(1))

1._at        : culture         =           1
               rand2           =           2

2._at        : culture         =           2
               rand2           =           2

3._at        : culture         =           3
               rand2           =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .7960584   .0196182    40.58   0.000     .7576074    .8345094
          2  |   .7093684   .0374284    18.95   0.000     .6360101    .7827267
          3  |   .7480732   .0307869    24.30   0.000     .6877319    .8084145
------------------------------------------------------------------------------

. est store notext

. 
. coefplot        (ext, msymbol(diamond) msize(small) col(black) ciopt(col(black))) ///
>                         (notext, msize(medium) col(gs8) ciopt(col(gs8))), ///
>         legend(off) citype(logit) ///
>         xlab(0(.1).9, format(%2.1f)) xtitle("Pr(Exploratory)", size(small)) ///
>         ylab(1 `" "Qualitative" "' 2 `" "Quantitative" "' ///
>                 3 `" "Mixed-Method" "', labsize(small)) grid(none)

. graph save Graph "output/figA3bm1.gph", replace
(file output/figA3bm1.gph saved)

. 
. est restore model
(results model are active now)

. margins if rand2 == 1, at(culture=(1 2 3) rand=(1)) post pr(out(2))

Predictive margins                              Number of obs     =        459
Model VCE    : Robust

Expression   : Pr(vig2outcome==confirmatory), predict(out(2))

1._at        : culture         =           1
               rand2           =           1

2._at        : culture         =           2
               rand2           =           1

3._at        : culture         =           3
               rand2           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1435294   .0535521     2.68   0.007     .0385693    .2484896
          2  |   .4605699   .0351993    13.08   0.000     .3915805    .5295592
          3  |   .3431858   .0281055    12.21   0.000     .2880999    .3982716
------------------------------------------------------------------------------

. est store ext

. est restore model
(results model are active now)

. margins if rand2 == 2, at(culture=(1 2 3) rand2=(2)) post pr(out(2))

Predictive margins                              Number of obs     =        442
Model VCE    : Robust

Expression   : Pr(vig2outcome==confirmatory), predict(out(2))

1._at        : culture         =           1
               rand2           =           2

2._at        : culture         =           2
               rand2           =           2

3._at        : culture         =           3
               rand2           =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0254313   .0176968     1.44   0.151    -.0092539    .0601164
          2  |   .2179217   .0341601     6.38   0.000     .1509692    .2848742
          3  |   .1114848   .0114354     9.75   0.000     .0890718    .1338978
------------------------------------------------------------------------------

. est store notext

. 
. coefplot        (ext, msymbol(diamond) msize(small) col(black) ciopt(col(black))) ///
>                         (notext, msize(medium) col(gs8) ciopt(col(gs8))), ///
>         legend(off) citype(logit) ///
>         xlab(0(.1).9, format(%2.1f)) xtitle("Pr(Confirmatory)", size(small)) ///
>         ylab(1 `" "Qualitative" "' 2 `" "Quantitative" "' ///
>                 3 `" "Mixed-Method" "', labsize(small)) grid(none)

. graph save Graph "output/figA3bm2.gph", replace
(file output/figA3bm2.gph saved)

. 
. est restore model
(results model are active now)

. margins if rand2 == 1, at(culture=(1 2 3) rand=(1)) post pr(out(3))

Predictive margins                              Number of obs     =        459
Model VCE    : Robust

Expression   : Pr(vig2outcome==process), predict(out(3))

1._at        : culture         =           1
               rand2           =           1

2._at        : culture         =           2
               rand2           =           1

3._at        : culture         =           3
               rand2           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .5899665   .0904236     6.52   0.000     .4127396    .7671934
          2  |   .3336059   .0271539    12.29   0.000     .2803852    .3868265
          3  |   .4693941   .0366476    12.81   0.000     .3975661    .5412221
------------------------------------------------------------------------------

. est store ext

. est restore model
(results model are active now)

. margins if rand2 == 2, at(culture=(1 2 3) rand2=(2)) post pr(out(3))

Predictive margins                              Number of obs     =        442
Model VCE    : Robust

Expression   : Pr(vig2outcome==process), predict(out(3))

1._at        : culture         =           1
               rand2           =           2

2._at        : culture         =           2
               rand2           =           2

3._at        : culture         =           3
               rand2           =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1785103   .0271384     6.58   0.000       .12532    .2317006
          2  |   .0727099   .0197519     3.68   0.000     .0339969    .1114229
          3  |    .140442   .0293908     4.78   0.000     .0828371    .1980469
------------------------------------------------------------------------------

. est store notext

. 
. coefplot        (ext, msymbol(diamond) msize(small) col(black) ciopt(col(black))) ///
>                         (notext, msize(medium) col(gs8) ciopt(col(gs8))), ///
>         legend(order(2 "extensively studied" 4 "not extensively studied") position(6) cols(2)) citype(logit) ///
>         xlab(0(.1).9, format(%2.1f)) xtitle("Pr(Process)", size(small)) ///
>         ylab(1 `" "Qualitative" "' 2 `" "Quantitative" "' ///
>                 3 `" "Mixed-Method" "', labsize(small)) grid(none)

. graph save Graph "output/figA3bm3.gph", replace
(file output/figA3bm3.gph saved)

. 
. graph combine "output/figA3bm1.gph" "output/figA3bm2.gph" "output/figA3bm3.gph", rows(3) imargin(small) title("") commonscheme graphregion(color(white) margin(small) fcolor(white)) xcommon ycommon

. graph save Graph "output/figA3b.gph", replace
(file output/figA3b.gph saved)

. graph export "output/figureA3b.tif", width(1000) replace
(file output/figureA3b.tif written in TIFF format)

. est clear 

. 
. *Figure A4: Treatment effect accross continents
. mlogit vig2outcome i.culture i.continent#i.rand2 i.gender i.statuscat age, robust cluster(country)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -780.31149  
Iteration 2:   log pseudolikelihood = -778.13686  
Iteration 3:   log pseudolikelihood = -778.13189  
Iteration 4:   log pseudolikelihood = -778.13189  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -778.13189               Pseudo R2         =     0.1838

                                              (Std. Err. adjusted for 11 clusters in country)
---------------------------------------------------------------------------------------------
                            |               Robust
                vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
exploratory                 |  (base outcome)
----------------------------+----------------------------------------------------------------
confirmatory                |
                    culture |
              Quantitative  |   1.639548   .3571546     4.59   0.000      .939538    2.339558
              Mixed-method  |   1.214922   .3182123     3.82   0.000      .591237    1.838606
                            |
            continent#rand2 |
EU#not extensively studied  |  -2.507984   .3568041    -7.03   0.000    -3.207307   -1.808661
    US#extensively studied  |  -.5037961   .3091409    -1.63   0.103    -1.109701    .1021088
US#not extensively studied  |  -2.645603   .3116384    -8.49   0.000    -3.256403   -2.034803
                            |
                     gender |
                      Male  |   .3089183   .1954948     1.58   0.114    -.0742444     .692081
      Prefer not to answer  |   .1547969   .4988005     0.31   0.756    -.8228341    1.132428
                            |
                  statuscat |
            Senior Scholar  |   .2560875    .627954     0.41   0.683    -.9746798    1.486855
            Junior Scholar  |  -.2010686   .9049712    -0.22   0.824     -1.97478    1.572642
                            |
                        age |  -.0078193   .0267023    -0.29   0.770    -.0601548    .0445162
                      _cons |  -.3411296   1.768645    -0.19   0.847     -3.80761    3.125351
----------------------------+----------------------------------------------------------------
process                     |
                    culture |
              Quantitative  |  -.4002679   .2914739    -1.37   0.170    -.9715462    .1710104
              Mixed-method  |  -.0278787   .2774832    -0.10   0.920    -.5717358    .5159784
                            |
            continent#rand2 |
EU#not extensively studied  |  -3.133346   .3662714    -8.55   0.000    -3.851225   -2.415467
    US#extensively studied  |  -.8469969   .3007172    -2.82   0.005    -1.436392   -.2576021
US#not extensively studied  |  -2.890877   .3123014    -9.26   0.000    -3.502977   -2.278778
                            |
                     gender |
                      Male  |   .0238022   .1389223     0.17   0.864    -.2484805    .2960849
      Prefer not to answer  |   .1699511   .5920684     0.29   0.774    -.9904816    1.330384
                            |
                  statuscat |
            Senior Scholar  |   .6104454   .6542116     0.93   0.351    -.6717857    1.892677
            Junior Scholar  |   .6393447   .7759267     0.82   0.410    -.8814438    2.160133
                            |
                        age |   .0207596   .0234326     0.89   0.376    -.0251674    .0666866
                      _cons |   .1177407   1.771748     0.07   0.947    -3.354822    3.590304
---------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2) at(continent=(1 2)) pr(out(1)) post

Average marginal effects                        Number of obs     =        901
Model VCE    : Robust

Expression   : Pr(vig2outcome==exploratory), predict(out(1))
dy/dx w.r.t. : 2.rand2

1._at        : continent       =           1

2._at        : continent       =           2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.rand2      |  (base outcome)
-------------+----------------------------------------------------------------
2.rand2      |
         _at |
          1  |   .5954969   .0452693    13.15   0.000     .5067706    .6842232
          2  |   .4753718   .0080908    58.75   0.000     .4595142    .4912294
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, scheme(plottig) xline(0, lpattern(dash) lcolor(black)) title(Exploratory, size(small)) coeflabels(1._at = "EU" 2._at = "US") xscale(range(-0.7 0.7)) xlabel(-0.7(0.1)0.7) nodraw mcolor(blac
> k) mfcolor(black) msymbol(diamond)  ciopts(lcolor(black))
(note:  alignstroke foreground not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke tick not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke grid not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke major_grid not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke minortick not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke axisline not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke background not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke plotregion not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1mark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1 not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1other not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke dotmark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  anglestyle symbol not found in scheme, default attributes used)
(note:  anglestyle symbol not found in scheme, default attributes used)
(note:  alignstroke p2mark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p2 not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p2other not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke xyline not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)

. graph save Graph "output/figA4m1.gph", replace
(file output/figA4m1.gph saved)

. est clear

. mlogit vig2outcome i.culture i.continent#i.rand2 i.gender i.statuscat age, robust cluster(country)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -780.31149  
Iteration 2:   log pseudolikelihood = -778.13686  
Iteration 3:   log pseudolikelihood = -778.13189  
Iteration 4:   log pseudolikelihood = -778.13189  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -778.13189               Pseudo R2         =     0.1838

                                              (Std. Err. adjusted for 11 clusters in country)
---------------------------------------------------------------------------------------------
                            |               Robust
                vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
exploratory                 |  (base outcome)
----------------------------+----------------------------------------------------------------
confirmatory                |
                    culture |
              Quantitative  |   1.639548   .3571546     4.59   0.000      .939538    2.339558
              Mixed-method  |   1.214922   .3182123     3.82   0.000      .591237    1.838606
                            |
            continent#rand2 |
EU#not extensively studied  |  -2.507984   .3568041    -7.03   0.000    -3.207307   -1.808661
    US#extensively studied  |  -.5037961   .3091409    -1.63   0.103    -1.109701    .1021088
US#not extensively studied  |  -2.645603   .3116384    -8.49   0.000    -3.256403   -2.034803
                            |
                     gender |
                      Male  |   .3089183   .1954948     1.58   0.114    -.0742444     .692081
      Prefer not to answer  |   .1547969   .4988005     0.31   0.756    -.8228341    1.132428
                            |
                  statuscat |
            Senior Scholar  |   .2560875    .627954     0.41   0.683    -.9746798    1.486855
            Junior Scholar  |  -.2010686   .9049712    -0.22   0.824     -1.97478    1.572642
                            |
                        age |  -.0078193   .0267023    -0.29   0.770    -.0601548    .0445162
                      _cons |  -.3411296   1.768645    -0.19   0.847     -3.80761    3.125351
----------------------------+----------------------------------------------------------------
process                     |
                    culture |
              Quantitative  |  -.4002679   .2914739    -1.37   0.170    -.9715462    .1710104
              Mixed-method  |  -.0278787   .2774832    -0.10   0.920    -.5717358    .5159784
                            |
            continent#rand2 |
EU#not extensively studied  |  -3.133346   .3662714    -8.55   0.000    -3.851225   -2.415467
    US#extensively studied  |  -.8469969   .3007172    -2.82   0.005    -1.436392   -.2576021
US#not extensively studied  |  -2.890877   .3123014    -9.26   0.000    -3.502977   -2.278778
                            |
                     gender |
                      Male  |   .0238022   .1389223     0.17   0.864    -.2484805    .2960849
      Prefer not to answer  |   .1699511   .5920684     0.29   0.774    -.9904816    1.330384
                            |
                  statuscat |
            Senior Scholar  |   .6104454   .6542116     0.93   0.351    -.6717857    1.892677
            Junior Scholar  |   .6393447   .7759267     0.82   0.410    -.8814438    2.160133
                            |
                        age |   .0207596   .0234326     0.89   0.376    -.0251674    .0666866
                      _cons |   .1177407   1.771748     0.07   0.947    -3.354822    3.590304
---------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2) at(continent=(1 2)) pr(out(2)) post

Average marginal effects                        Number of obs     =        901
Model VCE    : Robust

Expression   : Pr(vig2outcome==confirmatory), predict(out(2))
dy/dx w.r.t. : 2.rand2

1._at        : continent       =           1

2._at        : continent       =           2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.rand2      |  (base outcome)
-------------+----------------------------------------------------------------
2.rand2      |
         _at |
          1  |  -.2001329    .043267    -4.63   0.000    -.2849347   -.1153311
          2  |  -.2331607   .0090493   -25.77   0.000     -.250897   -.2154245
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, scheme(plottig) xline(0, lpattern(dash) lcolor(black)) title(Confirmatory, size(small)) coeflabels(1._at = "EU" 2._at = "US") xscale(range(-0.7 0.7)) xlabel(-0.7(0.1)0.7) nodraw mcolor(bla
> ck) mfcolor(black) msymbol(diamond)  ciopts(lcolor(black))
(note:  alignstroke foreground not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke tick not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke grid not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke major_grid not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke minortick not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke axisline not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke background not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke plotregion not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1mark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1 not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1other not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke dotmark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  anglestyle symbol not found in scheme, default attributes used)
(note:  anglestyle symbol not found in scheme, default attributes used)
(note:  alignstroke p2mark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p2 not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p2other not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke xyline not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)

. graph save Graph "output/figA4m2.gph", replace
(file output/figA4m2.gph saved)

. est clear

. mlogit vig2outcome i.culture i.continent#i.rand2 i.gender i.statuscat age, robust cluster(country)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -780.31149  
Iteration 2:   log pseudolikelihood = -778.13686  
Iteration 3:   log pseudolikelihood = -778.13189  
Iteration 4:   log pseudolikelihood = -778.13189  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -778.13189               Pseudo R2         =     0.1838

                                              (Std. Err. adjusted for 11 clusters in country)
---------------------------------------------------------------------------------------------
                            |               Robust
                vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
exploratory                 |  (base outcome)
----------------------------+----------------------------------------------------------------
confirmatory                |
                    culture |
              Quantitative  |   1.639548   .3571546     4.59   0.000      .939538    2.339558
              Mixed-method  |   1.214922   .3182123     3.82   0.000      .591237    1.838606
                            |
            continent#rand2 |
EU#not extensively studied  |  -2.507984   .3568041    -7.03   0.000    -3.207307   -1.808661
    US#extensively studied  |  -.5037961   .3091409    -1.63   0.103    -1.109701    .1021088
US#not extensively studied  |  -2.645603   .3116384    -8.49   0.000    -3.256403   -2.034803
                            |
                     gender |
                      Male  |   .3089183   .1954948     1.58   0.114    -.0742444     .692081
      Prefer not to answer  |   .1547969   .4988005     0.31   0.756    -.8228341    1.132428
                            |
                  statuscat |
            Senior Scholar  |   .2560875    .627954     0.41   0.683    -.9746798    1.486855
            Junior Scholar  |  -.2010686   .9049712    -0.22   0.824     -1.97478    1.572642
                            |
                        age |  -.0078193   .0267023    -0.29   0.770    -.0601548    .0445162
                      _cons |  -.3411296   1.768645    -0.19   0.847     -3.80761    3.125351
----------------------------+----------------------------------------------------------------
process                     |
                    culture |
              Quantitative  |  -.4002679   .2914739    -1.37   0.170    -.9715462    .1710104
              Mixed-method  |  -.0278787   .2774832    -0.10   0.920    -.5717358    .5159784
                            |
            continent#rand2 |
EU#not extensively studied  |  -3.133346   .3662714    -8.55   0.000    -3.851225   -2.415467
    US#extensively studied  |  -.8469969   .3007172    -2.82   0.005    -1.436392   -.2576021
US#not extensively studied  |  -2.890877   .3123014    -9.26   0.000    -3.502977   -2.278778
                            |
                     gender |
                      Male  |   .0238022   .1389223     0.17   0.864    -.2484805    .2960849
      Prefer not to answer  |   .1699511   .5920684     0.29   0.774    -.9904816    1.330384
                            |
                  statuscat |
            Senior Scholar  |   .6104454   .6542116     0.93   0.351    -.6717857    1.892677
            Junior Scholar  |   .6393447   .7759267     0.82   0.410    -.8814438    2.160133
                            |
                        age |   .0207596   .0234326     0.89   0.376    -.0251674    .0666866
                      _cons |   .1177407   1.771748     0.07   0.947    -3.354822    3.590304
---------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2) at(continent=(1 2)) pr(out(3)) post

Average marginal effects                        Number of obs     =        901
Model VCE    : Robust

Expression   : Pr(vig2outcome==process), predict(out(3))
dy/dx w.r.t. : 2.rand2

1._at        : continent       =           1

2._at        : continent       =           2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.rand2      |  (base outcome)
-------------+----------------------------------------------------------------
2.rand2      |
         _at |
          1  |   -.395364   .0460234    -8.59   0.000    -.4855683   -.3051598
          2  |   -.242211   .0070085   -34.56   0.000    -.2559474   -.2284746
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, scheme(plottig) xline(0, lpattern(dash) lcolor(black)) title(Process, size(small)) coeflabels(1._at = "EU" 2._at = "US") xscale(range(-0.7 0.7)) xlabel(-0.7(0.1)0.7) nodraw mcolor(black) m
> fcolor(black) msymbol(diamond)  ciopts(lcolor(black))
(note:  alignstroke foreground not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke tick not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke grid not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke major_grid not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke minortick not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke axisline not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke background not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke plotregion not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1mark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1 not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1other not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke dotmark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  anglestyle symbol not found in scheme, default attributes used)
(note:  anglestyle symbol not found in scheme, default attributes used)
(note:  alignstroke p2mark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p2 not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p2other not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke xyline not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)

. graph save Graph "output/figA4m3.gph", replace
(file output/figA4m3.gph saved)

. est clear

. graph combine "output/figA4m1.gph" "output/figA4m2.gph" "output/figA4m3.gph", rows(3) imargin(small) title("") commonscheme graphregion(color(white) margin(small) fcolor(white)) ycommon

. graph save Graph "output/figA4.gph", replace
(file output/figA4.gph saved)

. graph export "output/figureA4.tif", width(1000) replace
(file output/figureA4.tif written in TIFF format)

. est clear

. 
. *Figure A5: Probability of choosing a specific goal of inference across continents and treatment conditions
. mlogit vig2outcome i.culture i.continent#i.rand2 i.gender i.statuscat age, robust cluster(country)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -780.31149  
Iteration 2:   log pseudolikelihood = -778.13686  
Iteration 3:   log pseudolikelihood = -778.13189  
Iteration 4:   log pseudolikelihood = -778.13189  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -778.13189               Pseudo R2         =     0.1838

                                              (Std. Err. adjusted for 11 clusters in country)
---------------------------------------------------------------------------------------------
                            |               Robust
                vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
exploratory                 |  (base outcome)
----------------------------+----------------------------------------------------------------
confirmatory                |
                    culture |
              Quantitative  |   1.639548   .3571546     4.59   0.000      .939538    2.339558
              Mixed-method  |   1.214922   .3182123     3.82   0.000      .591237    1.838606
                            |
            continent#rand2 |
EU#not extensively studied  |  -2.507984   .3568041    -7.03   0.000    -3.207307   -1.808661
    US#extensively studied  |  -.5037961   .3091409    -1.63   0.103    -1.109701    .1021088
US#not extensively studied  |  -2.645603   .3116384    -8.49   0.000    -3.256403   -2.034803
                            |
                     gender |
                      Male  |   .3089183   .1954948     1.58   0.114    -.0742444     .692081
      Prefer not to answer  |   .1547969   .4988005     0.31   0.756    -.8228341    1.132428
                            |
                  statuscat |
            Senior Scholar  |   .2560875    .627954     0.41   0.683    -.9746798    1.486855
            Junior Scholar  |  -.2010686   .9049712    -0.22   0.824     -1.97478    1.572642
                            |
                        age |  -.0078193   .0267023    -0.29   0.770    -.0601548    .0445162
                      _cons |  -.3411296   1.768645    -0.19   0.847     -3.80761    3.125351
----------------------------+----------------------------------------------------------------
process                     |
                    culture |
              Quantitative  |  -.4002679   .2914739    -1.37   0.170    -.9715462    .1710104
              Mixed-method  |  -.0278787   .2774832    -0.10   0.920    -.5717358    .5159784
                            |
            continent#rand2 |
EU#not extensively studied  |  -3.133346   .3662714    -8.55   0.000    -3.851225   -2.415467
    US#extensively studied  |  -.8469969   .3007172    -2.82   0.005    -1.436392   -.2576021
US#not extensively studied  |  -2.890877   .3123014    -9.26   0.000    -3.502977   -2.278778
                            |
                     gender |
                      Male  |   .0238022   .1389223     0.17   0.864    -.2484805    .2960849
      Prefer not to answer  |   .1699511   .5920684     0.29   0.774    -.9904816    1.330384
                            |
                  statuscat |
            Senior Scholar  |   .6104454   .6542116     0.93   0.351    -.6717857    1.892677
            Junior Scholar  |   .6393447   .7759267     0.82   0.410    -.8814438    2.160133
                            |
                        age |   .0207596   .0234326     0.89   0.376    -.0251674    .0666866
                      _cons |   .1177407   1.771748     0.07   0.947    -3.354822    3.590304
---------------------------------------------------------------------------------------------

. est store model

. 
. margins if rand2 == 1, at(continent=(1 2)) post pr(out(1))

Predictive margins                              Number of obs     =        459
Model VCE    : Robust

Expression   : Pr(vig2outcome==exploratory), predict(out(1))

1._at        : continent       =           1

2._at        : continent       =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1484371    .040245     3.69   0.000     .0695584    .2273159
          2  |   .2585563   .0083165    31.09   0.000     .2422563    .2748563
------------------------------------------------------------------------------

. est store       ext

. est restore model
(results model are active now)

. margins if rand2 == 2, at(continent=(1 2)) post pr(out(1))

Predictive margins                              Number of obs     =        442
Model VCE    : Robust

Expression   : Pr(vig2outcome==exploratory), predict(out(1))

1._at        : continent       =           1

2._at        : continent       =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .7465454   .0232862    32.06   0.000     .7009053    .7921856
          2  |   .7369186   .0048377   152.33   0.000     .7274369    .7464002
------------------------------------------------------------------------------

. est store       notext

. 
. coefplot        (ext, msymbol(diamond) msize(small) col(black) ciopt(col(black))) ///
>                         (notext, msize(medium) col(gs8) ciopt(col(gs8))), ///
>         legend(off) ///
>         xlab(0(.1).9, format(%2.1f)) xtitle("Pr(Exploratory)", size(small)) ///
>         ylab(1 `" "EU" "' 2 `" "US" "', labsize(small)) grid(none)

. graph save Graph "output/figA5m1.gph", replace
(file output/figA5m1.gph saved)

. 
. est restore model
(results model are active now)

. margins if rand2 == 1, at(continent=(1 2)) post pr(out(2))

Predictive margins                              Number of obs     =        459
Model VCE    : Robust

Expression   : Pr(vig2outcome==confirmatory), predict(out(2))

1._at        : continent       =           1

2._at        : continent       =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |     .34763   .0304695    11.41   0.000     .2879108    .4073491
          2  |   .3618221   .0086618    41.77   0.000     .3448454    .3787989
------------------------------------------------------------------------------

. est store       ext

. est restore model
(results model are active now)

. margins if rand2 == 2, at(continent=(1 2)) post pr(out(2))

Predictive margins                              Number of obs     =        442
Model VCE    : Robust

Expression   : Pr(vig2outcome==confirmatory), predict(out(2))

1._at        : continent       =           1

2._at        : continent       =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1410485   .0276038     5.11   0.000     .0869461    .1951509
          2  |   .1221512   .0033307    36.67   0.000     .1156232    .1286792
------------------------------------------------------------------------------

. est store       notext

. coefplot        (ext, msymbol(diamond) msize(small) col(black) ciopt(col(black))) ///
>                         (notext, msize(medium) col(gs8) ciopt(col(gs8))), ///
>         legend(off) ///
>         xlab(0(.1).9, format(%2.1f)) xtitle("Pr(Confirmatory)", size(small)) ///
>         ylab(1 `" "EU" "' 2 `" "US" "', labsize(small)) grid(none)

. graph save Graph "output/figA5m2.gph", replace
(file output/figA5m2.gph saved)

.         
. est restore model
(results model are active now)

. margins if rand2 == 1, at(continent=(1 2)) post pr(out(3))

Predictive margins                              Number of obs     =        459
Model VCE    : Robust

Expression   : Pr(vig2outcome==process), predict(out(3))

1._at        : continent       =           1

2._at        : continent       =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .5039329   .0415018    12.14   0.000     .4225908     .585275
          2  |   .3796216   .0088998    42.65   0.000     .3621782    .3970649
------------------------------------------------------------------------------

. est store       ext

. est restore model
(results model are active now)

. margins if rand2 == 2, at(continent=(1 2)) post pr(out(3))

Predictive margins                              Number of obs     =        442
Model VCE    : Robust

Expression   : Pr(vig2outcome==process), predict(out(3))

1._at        : continent       =           1

2._at        : continent       =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1124061   .0120051     9.36   0.000     .0888765    .1359357
          2  |   .1409303   .0043846    32.14   0.000     .1323365     .149524
------------------------------------------------------------------------------

. est store       notext

. coefplot        (ext, msymbol(diamond) msize(small) col(black) ciopt(col(black))) ///
>                         (notext, msize(medium) col(gs8) ciopt(col(gs8))), ///
>         legend(order(2 "extensively studied" 4 "not extensively studied") position(6) cols(2)) ///
>         xlab(0(.1).9, format(%2.1f)) xtitle("Pr(Process)", size(small)) ///
>         ylab(1 `" "EU" "' 2 `" "US" "', labsize(small)) grid(none)

. graph save Graph "output/figA5m3.gph", replace
(file output/figA5m3.gph saved)

. 
. graph combine "output/figA5m1.gph" "output/figA5m2.gph" "output/figA5m3.gph", rows(3) imargin(small) title("") commonscheme graphregion(color(white) margin(small) fcolor(white)) xcommon ycommon

. graph save Graph "output/figA5.gph", replace
(file output/figA5.gph saved)

. graph export "output/figureA5.tif", width(1000) replace
(file output/figureA5.tif written in TIFF format)

. 
. ***Wald tests of differences between combinations of treatment and continents
. est restore model
(results model are active now)

. margins, over(rand2) at(continent=(1 2)) post

Predictive margins                              Number of obs     =        901
Model VCE    : Robust

over         : rand2
1._predict   : Pr(vig2outcome==exploratory), predict(pr outcome(1))
2._predict   : Pr(vig2outcome==confirmatory), predict(pr outcome(2))
3._predict   : Pr(vig2outcome==process), predict(pr outcome(3))

1._at        : 1.rand2
                   continent       =           1
               2.rand2
                   continent       =           1

2._at        : 1.rand2
                   continent       =           2
               2.rand2
                   continent       =           2

----------------------------------------------------------------------------------------------
                             |            Delta-method
                             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
          _predict#_at#rand2 |
    1#1#extensively studied  |   .1484371    .040245     3.69   0.000     .0695584    .2273159
1#1#not extensively studied  |   .7465454   .0232862    32.06   0.000     .7009053    .7921856
    1#2#extensively studied  |   .2585563   .0083165    31.09   0.000     .2422563    .2748563
1#2#not extensively studied  |   .7369186   .0048377   152.33   0.000     .7274369    .7464002
    2#1#extensively studied  |     .34763   .0304695    11.41   0.000     .2879108    .4073491
2#1#not extensively studied  |   .1410485   .0276038     5.11   0.000     .0869461    .1951509
    2#2#extensively studied  |   .3618221   .0086618    41.77   0.000     .3448454    .3787989
2#2#not extensively studied  |   .1221512   .0033307    36.67   0.000     .1156232    .1286792
    3#1#extensively studied  |   .5039329   .0415018    12.14   0.000     .4225908     .585275
3#1#not extensively studied  |   .1124061   .0120051     9.36   0.000     .0888765    .1359357
    3#2#extensively studied  |   .3796216   .0088998    42.65   0.000     .3621782    .3970649
3#2#not extensively studied  |   .1409303   .0043846    32.14   0.000     .1323365     .149524
----------------------------------------------------------------------------------------------

. *Difference between EU and US researchers in choosing a process oriented outcome given an understudied research topic
. mlincom 12-10   

             |   lincom    pvalue        ll        ul 
-------------+----------------------------------------
           1 |    0.029     0.031     0.003     0.054 

. est clear

. 
. ***Figures A6-A8: Manipulation Checks
. *Figure A6: Marginal effect of experimental treatment and research cultures on agreeing with manipulation check question
. logit Manipulate1n i.rand2 i.gender i.statuscat age i.culture i.continent, robust cluster(country)

Iteration 0:   log pseudolikelihood = -220.50598  
Iteration 1:   log pseudolikelihood = -215.44874  
Iteration 2:   log pseudolikelihood = -215.20972  
Iteration 3:   log pseudolikelihood = -215.20889  
Iteration 4:   log pseudolikelihood = -215.20889  

Logistic regression                             Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -215.20889               Pseudo R2         =     0.0240

                                           (Std. Err. adjusted for 11 clusters in country)
------------------------------------------------------------------------------------------
                         |               Robust
            Manipulate1n |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                   rand2 |
not extensively studied  |  -.0764005   .1286342    -0.59   0.553    -.3285188    .1757178
                         |
                  gender |
                   Male  |   .1415334   .2584521     0.55   0.584    -.3650234    .6480901
   Prefer not to answer  |   -.768302   1.082595    -0.71   0.478    -2.890149    1.353545
                         |
               statuscat |
         Senior Scholar  |   .8224245   .4752211     1.73   0.084    -.1089918    1.753841
         Junior Scholar  |   .9805972   .6560363     1.49   0.135    -.3052103    2.266405
                         |
                     age |   .0169941   .0198492     0.86   0.392    -.0219096    .0558977
                         |
                 culture |
           Quantitative  |   .3228302   .5169277     0.62   0.532    -.6903296     1.33599
           Mixed-method  |  -.3711769   .7151837    -0.52   0.604    -1.772911    1.030557
                         |
               continent |
                     US  |  -.1563587   .2960405    -0.53   0.597    -.7365874      .42387
                   _cons |  -3.911139   1.259786    -3.10   0.002    -6.380274   -1.442003
------------------------------------------------------------------------------------------

. margins, dydx(rand2 culture) post atmeans

Conditional marginal effects                    Number of obs     =        901
Model VCE    : Robust

Expression   : Pr(Manipulate1n), predict()
dy/dx w.r.t. : 2.rand2 2.culture 3.culture
at           : 1.rand2         =     .509434 (mean)
               2.rand2         =     .490566 (mean)
               1.gender        =    .3240844 (mean)
               2.gender        =    .6426193 (mean)
               3.gender        =    .0332963 (mean)
               1.statuscat     =    .2241953 (mean)
               2.statuscat     =    .3174251 (mean)
               3.statuscat     =    .4583796 (mean)
               age             =    35.02415 (mean)
               1.culture       =    .1664817 (mean)
               2.culture       =    .3429523 (mean)
               3.culture       =     .490566 (mean)
               1.continent     =    .4827969 (mean)
               2.continent     =    .5172031 (mean)

------------------------------------------------------------------------------------------
                         |            Delta-method
                         |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                   rand2 |
not extensively studied  |  -.0043902   .0073242    -0.60   0.549    -.0187453    .0099649
                         |
                 culture |
           Quantitative  |    .022747   .0314637     0.72   0.470    -.0389206    .0844147
           Mixed-method  |  -.0193663   .0405882    -0.48   0.633    -.0989177    .0601852
------------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, xline(0, lpattern(dash) lcolor(black)) title("", size(small)) xscale(range(-0.2 0.2)) xlabel(-0.2(0.05)0.2) mcolor(black) mfcolor(black) msymbol(diamond)  ciopts(lcolor(black))

. graph save Graph "output/figA6.gph", replace
(file output/figA6.gph saved)

. graph export "output/figureA6.tif", width(1000) replace
(file output/figureA6.tif written in TIFF format)

. est clear

. 
. *Figure A7: Replicate Figure 2 with manipulation check included as covariate
. mlogit vig2outcome i.rand2 i.culture i.gender i.statuscat age i.continent i.Manipulate1n, robust cluster(country)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -782.40806  
Iteration 2:   log pseudolikelihood = -781.25217  
Iteration 3:   log pseudolikelihood = -781.24761  
Iteration 4:   log pseudolikelihood = -781.24761  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -781.24761               Pseudo R2         =     0.1805

                                           (Std. Err. adjusted for 11 clusters in country)
------------------------------------------------------------------------------------------
                         |               Robust
             vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
exploratory              |  (base outcome)
-------------------------+----------------------------------------------------------------
confirmatory             |
                   rand2 |
not extensively studied  |  -2.287418   .1623179   -14.09   0.000    -2.605555    -1.96928
                         |
                 culture |
           Quantitative  |   1.633923    .357029     4.58   0.000     .9341587    2.333687
           Mixed-method  |   1.219023      .3185     3.83   0.000     .5947747    1.843272
                         |
                  gender |
                   Male  |    .305462   .1977754     1.54   0.122    -.0821706    .6930946
   Prefer not to answer  |   .1690508   .4833863     0.35   0.727    -.7783689    1.116471
                         |
               statuscat |
         Senior Scholar  |   .2113056   .6136102     0.34   0.731    -.9913483     1.41396
         Junior Scholar  |  -.2337255   .8902592    -0.26   0.793    -1.978601     1.51115
                         |
                     age |  -.0087314   .0261847    -0.33   0.739    -.0600525    .0425897
                         |
               continent |
                     US  |  -.1976948   .1842269    -1.07   0.283    -.5587729    .1633833
                         |
            Manipulate1n |
                    Yes  |   .1014412   .2012471     0.50   0.614    -.2929958    .4958782
                   _cons |  -.4748862   1.616678    -0.29   0.769    -3.643517    2.693744
-------------------------+----------------------------------------------------------------
process                  |
                   rand2 |
not extensively studied  |   -2.56848   .3411558    -7.53   0.000    -3.237134   -1.899827
                         |
                 culture |
           Quantitative  |  -.4102198   .2809461    -1.46   0.144    -.9608641    .1404246
           Mixed-method  |  -.0376184   .2602958    -0.14   0.885    -.5477888     .472552
                         |
                  gender |
                   Male  |   .0270667   .1403738     0.19   0.847    -.2480608    .3021942
   Prefer not to answer  |   .1733636   .5540992     0.31   0.754    -.9126508    1.259378
                         |
               statuscat |
         Senior Scholar  |   .5575509   .6241163     0.89   0.372    -.6656945    1.780796
         Junior Scholar  |    .624227   .7395332     0.84   0.399    -.8252315    2.073686
                         |
                     age |   .0198079   .0218231     0.91   0.364    -.0229646    .0625804
                         |
               continent |
                     US  |  -.3878222   .1495705    -2.59   0.010    -.6809749   -.0946695
                         |
            Manipulate1n |
                    Yes  |  -.3691932   .2941317    -1.26   0.209    -.9456807    .2072944
                   _cons |  -.0543643   1.539343    -0.04   0.972    -3.071422    2.962693
------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2 culture) pr(out(1)) post atmeans

Conditional marginal effects                    Number of obs     =        901
Model VCE    : Robust

Expression   : Pr(vig2outcome==exploratory), predict(out(1))
dy/dx w.r.t. : 2.rand2 2.culture 3.culture
at           : 1.rand2         =     .509434 (mean)
               2.rand2         =     .490566 (mean)
               1.culture       =    .1664817 (mean)
               2.culture       =    .3429523 (mean)
               3.culture       =     .490566 (mean)
               1.gender        =    .3240844 (mean)
               2.gender        =    .6426193 (mean)
               3.gender        =    .0332963 (mean)
               1.statuscat     =    .2241953 (mean)
               2.statuscat     =    .3174251 (mean)
               3.statuscat     =    .4583796 (mean)
               age             =    35.02415 (mean)
               1.continent     =    .4827969 (mean)
               2.continent     =    .5172031 (mean)
               0.Manipul~1n    =    .9334073 (mean)
               1.Manipul~1n    =    .0665927 (mean)

------------------------------------------------------------------------------------------
                         |            Delta-method
                         |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                   rand2 |
not extensively studied  |   .5433647   .0399458    13.60   0.000     .4650723    .6216571
                         |
                 culture |
           Quantitative  |  -.0997514   .0677125    -1.47   0.141    -.2324655    .0329626
           Mixed-method  |  -.0862205   .0604369    -1.43   0.154    -.2046748    .0322337
------------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, xline(0, lpattern(dash) lcolor(black)) title(Exploratory, size(small)) xscale(range(0.6)) xlabel(-0.6(0.1)0.6) nodraw mcolor(black) mfcolor(black) msymbol(diamond)  ciopts(lcolor(black))

. graph save Graph "output/figA7m1.gph", replace
(file output/figA7m1.gph saved)

. mlogit vig2outcome i.rand2 i.culture i.gender i.statuscat age i.continent i.Manipulate1n, robust cluster(country)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -782.40806  
Iteration 2:   log pseudolikelihood = -781.25217  
Iteration 3:   log pseudolikelihood = -781.24761  
Iteration 4:   log pseudolikelihood = -781.24761  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -781.24761               Pseudo R2         =     0.1805

                                           (Std. Err. adjusted for 11 clusters in country)
------------------------------------------------------------------------------------------
                         |               Robust
             vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
exploratory              |  (base outcome)
-------------------------+----------------------------------------------------------------
confirmatory             |
                   rand2 |
not extensively studied  |  -2.287418   .1623179   -14.09   0.000    -2.605555    -1.96928
                         |
                 culture |
           Quantitative  |   1.633923    .357029     4.58   0.000     .9341587    2.333687
           Mixed-method  |   1.219023      .3185     3.83   0.000     .5947747    1.843272
                         |
                  gender |
                   Male  |    .305462   .1977754     1.54   0.122    -.0821706    .6930946
   Prefer not to answer  |   .1690508   .4833863     0.35   0.727    -.7783689    1.116471
                         |
               statuscat |
         Senior Scholar  |   .2113056   .6136102     0.34   0.731    -.9913483     1.41396
         Junior Scholar  |  -.2337255   .8902592    -0.26   0.793    -1.978601     1.51115
                         |
                     age |  -.0087314   .0261847    -0.33   0.739    -.0600525    .0425897
                         |
               continent |
                     US  |  -.1976948   .1842269    -1.07   0.283    -.5587729    .1633833
                         |
            Manipulate1n |
                    Yes  |   .1014412   .2012471     0.50   0.614    -.2929958    .4958782
                   _cons |  -.4748862   1.616678    -0.29   0.769    -3.643517    2.693744
-------------------------+----------------------------------------------------------------
process                  |
                   rand2 |
not extensively studied  |   -2.56848   .3411558    -7.53   0.000    -3.237134   -1.899827
                         |
                 culture |
           Quantitative  |  -.4102198   .2809461    -1.46   0.144    -.9608641    .1404246
           Mixed-method  |  -.0376184   .2602958    -0.14   0.885    -.5477888     .472552
                         |
                  gender |
                   Male  |   .0270667   .1403738     0.19   0.847    -.2480608    .3021942
   Prefer not to answer  |   .1733636   .5540992     0.31   0.754    -.9126508    1.259378
                         |
               statuscat |
         Senior Scholar  |   .5575509   .6241163     0.89   0.372    -.6656945    1.780796
         Junior Scholar  |    .624227   .7395332     0.84   0.399    -.8252315    2.073686
                         |
                     age |   .0198079   .0218231     0.91   0.364    -.0229646    .0625804
                         |
               continent |
                     US  |  -.3878222   .1495705    -2.59   0.010    -.6809749   -.0946695
                         |
            Manipulate1n |
                    Yes  |  -.3691932   .2941317    -1.26   0.209    -.9456807    .2072944
                   _cons |  -.0543643   1.539343    -0.04   0.972    -3.071422    2.962693
------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2 culture) pr(out(2)) post atmeans

Conditional marginal effects                    Number of obs     =        901
Model VCE    : Robust

Expression   : Pr(vig2outcome==confirmatory), predict(out(2))
dy/dx w.r.t. : 2.rand2 2.culture 3.culture
at           : 1.rand2         =     .509434 (mean)
               2.rand2         =     .490566 (mean)
               1.culture       =    .1664817 (mean)
               2.culture       =    .3429523 (mean)
               3.culture       =     .490566 (mean)
               1.gender        =    .3240844 (mean)
               2.gender        =    .6426193 (mean)
               3.gender        =    .0332963 (mean)
               1.statuscat     =    .2241953 (mean)
               2.statuscat     =    .3174251 (mean)
               3.statuscat     =    .4583796 (mean)
               age             =    35.02415 (mean)
               1.continent     =    .4827969 (mean)
               2.continent     =    .5172031 (mean)
               0.Manipul~1n    =    .9334073 (mean)
               1.Manipul~1n    =    .0665927 (mean)

------------------------------------------------------------------------------------------
                         |            Delta-method
                         |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                   rand2 |
not extensively studied  |  -.2147203   .0228964    -9.38   0.000    -.2595964   -.1698442
                         |
                 culture |
           Quantitative  |   .2724069   .0500199     5.45   0.000     .1743697    .3704441
           Mixed-method  |   .1580569    .035689     4.43   0.000     .0881077    .2280061
------------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, scheme(s1mono) xline(0, lpattern(dash) lcolor(black)) title(Confirmatory, size(small)) xscale(range(0.6)) xlabel(-0.6(0.1)0.6) nodraw mcolor(black) mfcolor(black) msymbol(diamond) ciopts(l
> color(black))

. graph save Graph "output/figA7m2.gph", replace
(file output/figA7m2.gph saved)

. mlogit vig2outcome i.rand2 i.culture i.gender i.statuscat age i.continent i.Manipulate1n, robust cluster(country)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -782.40806  
Iteration 2:   log pseudolikelihood = -781.25217  
Iteration 3:   log pseudolikelihood = -781.24761  
Iteration 4:   log pseudolikelihood = -781.24761  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -781.24761               Pseudo R2         =     0.1805

                                           (Std. Err. adjusted for 11 clusters in country)
------------------------------------------------------------------------------------------
                         |               Robust
             vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
exploratory              |  (base outcome)
-------------------------+----------------------------------------------------------------
confirmatory             |
                   rand2 |
not extensively studied  |  -2.287418   .1623179   -14.09   0.000    -2.605555    -1.96928
                         |
                 culture |
           Quantitative  |   1.633923    .357029     4.58   0.000     .9341587    2.333687
           Mixed-method  |   1.219023      .3185     3.83   0.000     .5947747    1.843272
                         |
                  gender |
                   Male  |    .305462   .1977754     1.54   0.122    -.0821706    .6930946
   Prefer not to answer  |   .1690508   .4833863     0.35   0.727    -.7783689    1.116471
                         |
               statuscat |
         Senior Scholar  |   .2113056   .6136102     0.34   0.731    -.9913483     1.41396
         Junior Scholar  |  -.2337255   .8902592    -0.26   0.793    -1.978601     1.51115
                         |
                     age |  -.0087314   .0261847    -0.33   0.739    -.0600525    .0425897
                         |
               continent |
                     US  |  -.1976948   .1842269    -1.07   0.283    -.5587729    .1633833
                         |
            Manipulate1n |
                    Yes  |   .1014412   .2012471     0.50   0.614    -.2929958    .4958782
                   _cons |  -.4748862   1.616678    -0.29   0.769    -3.643517    2.693744
-------------------------+----------------------------------------------------------------
process                  |
                   rand2 |
not extensively studied  |   -2.56848   .3411558    -7.53   0.000    -3.237134   -1.899827
                         |
                 culture |
           Quantitative  |  -.4102198   .2809461    -1.46   0.144    -.9608641    .1404246
           Mixed-method  |  -.0376184   .2602958    -0.14   0.885    -.5477888     .472552
                         |
                  gender |
                   Male  |   .0270667   .1403738     0.19   0.847    -.2480608    .3021942
   Prefer not to answer  |   .1733636   .5540992     0.31   0.754    -.9126508    1.259378
                         |
               statuscat |
         Senior Scholar  |   .5575509   .6241163     0.89   0.372    -.6656945    1.780796
         Junior Scholar  |    .624227   .7395332     0.84   0.399    -.8252315    2.073686
                         |
                     age |   .0198079   .0218231     0.91   0.364    -.0229646    .0625804
                         |
               continent |
                     US  |  -.3878222   .1495705    -2.59   0.010    -.6809749   -.0946695
                         |
            Manipulate1n |
                    Yes  |  -.3691932   .2941317    -1.26   0.209    -.9456807    .2072944
                   _cons |  -.0543643   1.539343    -0.04   0.972    -3.071422    2.962693
------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2 culture) pr(out(3)) post atmeans

Conditional marginal effects                    Number of obs     =        901
Model VCE    : Robust

Expression   : Pr(vig2outcome==process), predict(out(3))
dy/dx w.r.t. : 2.rand2 2.culture 3.culture
at           : 1.rand2         =     .509434 (mean)
               2.rand2         =     .490566 (mean)
               1.culture       =    .1664817 (mean)
               2.culture       =    .3429523 (mean)
               3.culture       =     .490566 (mean)
               1.gender        =    .3240844 (mean)
               2.gender        =    .6426193 (mean)
               3.gender        =    .0332963 (mean)
               1.statuscat     =    .2241953 (mean)
               2.statuscat     =    .3174251 (mean)
               3.statuscat     =    .4583796 (mean)
               age             =    35.02415 (mean)
               1.continent     =    .4827969 (mean)
               2.continent     =    .5172031 (mean)
               0.Manipul~1n    =    .9334073 (mean)
               1.Manipul~1n    =    .0665927 (mean)

------------------------------------------------------------------------------------------
                         |            Delta-method
                         |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                   rand2 |
not extensively studied  |  -.3286444   .0483566    -6.80   0.000    -.4234216   -.2338673
                         |
                 culture |
           Quantitative  |  -.1726555   .0660981    -2.61   0.009    -.3022054   -.0431055
           Mixed-method  |  -.0718363   .0612602    -1.17   0.241    -.1919041    .0482315
------------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, scheme(s1mono) xline(0, lpattern(dash) lcolor(black)) title(Process, size(small)) xscale(range(0.6)) xlabel(-0.6(0.1)0.6) nodraw mcolor(black) mfcolor(black) msymbol(diamond) ciopts(lcolor
> (black))

. graph save Graph "output/figA7m3.gph", replace
(file output/figA7m3.gph saved)

. graph combine "output/figA7m1.gph" "output/figA7m2.gph" "output/figA7m3.gph", rows(3) imargin(small) title("") commonscheme graphregion(color(white) margin(small) fcolor(white)) xcommon ycommon

. graph save Graph "output/figA7.gph", replace
(file output/figA7.gph saved)

. graph export "output/figureA7.tif", width(1000) replace
(file output/figureA7.tif written in TIFF format)

. 
. est clear

. 
. *Figure A8:regression models with interaction effects of treatment and manipulation check
. mlogit vig2outcome i.Manipulate1n#i.rand2 i.gender i.statuscat age i.culture i.continent, robust cluster(country)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -782.23837  
Iteration 2:   log pseudolikelihood =  -780.5249  
Iteration 3:   log pseudolikelihood =  -780.4886  
Iteration 4:   log pseudolikelihood = -780.48843  
Iteration 5:   log pseudolikelihood = -780.48843  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -780.48843               Pseudo R2         =     0.1813

                                               (Std. Err. adjusted for 11 clusters in country)
----------------------------------------------------------------------------------------------
                             |               Robust
                 vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
exploratory                  |  (base outcome)
-----------------------------+----------------------------------------------------------------
confirmatory                 |
          Manipulate1n#rand2 |
 No#not extensively studied  |  -2.283806   .1682152   -13.58   0.000    -2.613502   -1.954111
    Yes#extensively studied  |    .225593   .3297945     0.68   0.494    -.4207924    .8719784
Yes#not extensively studied  |  -2.134446   .4075888    -5.24   0.000    -2.933305   -1.335587
                             |
                      gender |
                       Male  |   .3054878    .199245     1.53   0.125    -.0850252    .6960007
       Prefer not to answer  |   .1642675   .4873865     0.34   0.736    -.7909924    1.119527
                             |
                   statuscat |
             Senior Scholar  |    .205336   .6005476     0.34   0.732    -.9717156    1.382388
             Junior Scholar  |  -.2373976     .86779    -0.27   0.784    -1.938235    1.463439
                             |
                         age |  -.0088562   .0255347    -0.35   0.729    -.0589033     .041191
                             |
                     culture |
               Quantitative  |    1.63364   .3634592     4.49   0.000     .9212732    2.346007
               Mixed-method  |    1.21857   .3224883     3.78   0.000     .5865046    1.850636
                             |
                   continent |
                         US  |  -.1992404   .1851546    -1.08   0.282    -.5621368    .1636561
                       _cons |  -.4736966   1.597426    -0.30   0.767    -3.604593      2.6572
-----------------------------+----------------------------------------------------------------
process                      |
          Manipulate1n#rand2 |
 No#not extensively studied  |  -2.519454   .3270133    -7.70   0.000    -3.160389    -1.87852
    Yes#extensively studied  |   -.075566   .3789556    -0.20   0.842    -.8183054    .6671735
Yes#not extensively studied  |  -3.839903   1.217101    -3.15   0.002    -6.225376    -1.45443
                             |
                      gender |
                       Male  |   .0245825   .1377532     0.18   0.858    -.2454087    .2945738
       Prefer not to answer  |     .15981   .5644893     0.28   0.777    -.9465688    1.266189
                             |
                   statuscat |
             Senior Scholar  |   .5366814   .6331684     0.85   0.397    -.7043058    1.777669
             Junior Scholar  |   .6002698   .7440214     0.81   0.420    -.8579852    2.058525
                             |
                         age |   .0190761   .0218905     0.87   0.384    -.0238285    .0619806
                             |
                     culture |
               Quantitative  |  -.4053594   .2846321    -1.42   0.154     -.963228    .1525093
               Mixed-method  |  -.0370435   .2649849    -0.14   0.889    -.5564042    .4823173
                             |
                   continent |
                         US  |  -.3897497   .1522433    -2.56   0.010    -.6881411   -.0913582
                       _cons |  -.0287149   1.549744    -0.02   0.985    -3.066158    3.008728
----------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2) at(Manipulate1n=(0 1)) pr(out(1)) post atmeans

Conditional marginal effects                    Number of obs     =        901
Model VCE    : Robust

Expression   : Pr(vig2outcome==exploratory), predict(out(1))
dy/dx w.r.t. : 2.rand2

1._at        : Manipulate1n    =           0
               1.rand2         =     .509434 (mean)
               2.rand2         =     .490566 (mean)
               1.gender        =    .3240844 (mean)
               2.gender        =    .6426193 (mean)
               3.gender        =    .0332963 (mean)
               1.statuscat     =    .2241953 (mean)
               2.statuscat     =    .3174251 (mean)
               3.statuscat     =    .4583796 (mean)
               age             =    35.02415 (mean)
               1.culture       =    .1664817 (mean)
               2.culture       =    .3429523 (mean)
               3.culture       =     .490566 (mean)
               1.continent     =    .4827969 (mean)
               2.continent     =    .5172031 (mean)

2._at        : Manipulate1n    =           1
               1.rand2         =     .509434 (mean)
               2.rand2         =     .490566 (mean)
               1.gender        =    .3240844 (mean)
               2.gender        =    .6426193 (mean)
               3.gender        =    .0332963 (mean)
               1.statuscat     =    .2241953 (mean)
               2.statuscat     =    .3174251 (mean)
               3.statuscat     =    .4583796 (mean)
               age             =    35.02415 (mean)
               1.culture       =    .1664817 (mean)
               2.culture       =    .3429523 (mean)
               3.culture       =     .490566 (mean)
               1.continent     =    .4827969 (mean)
               2.continent     =    .5172031 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.rand2      |  (base outcome)
-------------+----------------------------------------------------------------
2.rand2      |
         _at |
          1  |   .5386953   .0369957    14.56   0.000      .466185    .6112056
          2  |   .6100989   .0900067     6.78   0.000     .4336891    .7865087
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, scheme(plottig) xline(0, lpattern(dash) lcolor(black)) title(Exploratory, size(small)) coeflabels(1._at = "No" 2._at = "Yes") xscale(range(-0.8 0.8)) xlabel(-0.8(0.1)0.8) nodraw mcolor(bla
> ck) mfcolor(black) msymbol(diamond)  ciopts(lcolor(black))
(note:  alignstroke foreground not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke tick not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke grid not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke major_grid not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke minortick not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke axisline not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke background not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke plotregion not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1mark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1 not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1other not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke dotmark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  anglestyle symbol not found in scheme, default attributes used)
(note:  anglestyle symbol not found in scheme, default attributes used)
(note:  alignstroke p2mark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p2 not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p2other not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke xyline not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)

. graph save Graph "output/figA8m1.gph", replace
(file output/figA8m1.gph saved)

. mlogit vig2outcome i.Manipulate1n#i.rand2 i.gender i.statuscat age i.culture i.continent, robust cluster(country)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -782.23837  
Iteration 2:   log pseudolikelihood =  -780.5249  
Iteration 3:   log pseudolikelihood =  -780.4886  
Iteration 4:   log pseudolikelihood = -780.48843  
Iteration 5:   log pseudolikelihood = -780.48843  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -780.48843               Pseudo R2         =     0.1813

                                               (Std. Err. adjusted for 11 clusters in country)
----------------------------------------------------------------------------------------------
                             |               Robust
                 vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
exploratory                  |  (base outcome)
-----------------------------+----------------------------------------------------------------
confirmatory                 |
          Manipulate1n#rand2 |
 No#not extensively studied  |  -2.283806   .1682152   -13.58   0.000    -2.613502   -1.954111
    Yes#extensively studied  |    .225593   .3297945     0.68   0.494    -.4207924    .8719784
Yes#not extensively studied  |  -2.134446   .4075888    -5.24   0.000    -2.933305   -1.335587
                             |
                      gender |
                       Male  |   .3054878    .199245     1.53   0.125    -.0850252    .6960007
       Prefer not to answer  |   .1642675   .4873865     0.34   0.736    -.7909924    1.119527
                             |
                   statuscat |
             Senior Scholar  |    .205336   .6005476     0.34   0.732    -.9717156    1.382388
             Junior Scholar  |  -.2373976     .86779    -0.27   0.784    -1.938235    1.463439
                             |
                         age |  -.0088562   .0255347    -0.35   0.729    -.0589033     .041191
                             |
                     culture |
               Quantitative  |    1.63364   .3634592     4.49   0.000     .9212732    2.346007
               Mixed-method  |    1.21857   .3224883     3.78   0.000     .5865046    1.850636
                             |
                   continent |
                         US  |  -.1992404   .1851546    -1.08   0.282    -.5621368    .1636561
                       _cons |  -.4736966   1.597426    -0.30   0.767    -3.604593      2.6572
-----------------------------+----------------------------------------------------------------
process                      |
          Manipulate1n#rand2 |
 No#not extensively studied  |  -2.519454   .3270133    -7.70   0.000    -3.160389    -1.87852
    Yes#extensively studied  |   -.075566   .3789556    -0.20   0.842    -.8183054    .6671735
Yes#not extensively studied  |  -3.839903   1.217101    -3.15   0.002    -6.225376    -1.45443
                             |
                      gender |
                       Male  |   .0245825   .1377532     0.18   0.858    -.2454087    .2945738
       Prefer not to answer  |     .15981   .5644893     0.28   0.777    -.9465688    1.266189
                             |
                   statuscat |
             Senior Scholar  |   .5366814   .6331684     0.85   0.397    -.7043058    1.777669
             Junior Scholar  |   .6002698   .7440214     0.81   0.420    -.8579852    2.058525
                             |
                         age |   .0190761   .0218905     0.87   0.384    -.0238285    .0619806
                             |
                     culture |
               Quantitative  |  -.4053594   .2846321    -1.42   0.154     -.963228    .1525093
               Mixed-method  |  -.0370435   .2649849    -0.14   0.889    -.5564042    .4823173
                             |
                   continent |
                         US  |  -.3897497   .1522433    -2.56   0.010    -.6881411   -.0913582
                       _cons |  -.0287149   1.549744    -0.02   0.985    -3.066158    3.008728
----------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2) at(Manipulate1n=(0 1)) pr(out(2)) post atmeans

Conditional marginal effects                    Number of obs     =        901
Model VCE    : Robust

Expression   : Pr(vig2outcome==confirmatory), predict(out(2))
dy/dx w.r.t. : 2.rand2

1._at        : Manipulate1n    =           0
               1.rand2         =     .509434 (mean)
               2.rand2         =     .490566 (mean)
               1.gender        =    .3240844 (mean)
               2.gender        =    .6426193 (mean)
               3.gender        =    .0332963 (mean)
               1.statuscat     =    .2241953 (mean)
               2.statuscat     =    .3174251 (mean)
               3.statuscat     =    .4583796 (mean)
               age             =    35.02415 (mean)
               1.culture       =    .1664817 (mean)
               2.culture       =    .3429523 (mean)
               3.culture       =     .490566 (mean)
               1.continent     =    .4827969 (mean)
               2.continent     =    .5172031 (mean)

2._at        : Manipulate1n    =           1
               1.rand2         =     .509434 (mean)
               2.rand2         =     .490566 (mean)
               1.gender        =    .3240844 (mean)
               2.gender        =    .6426193 (mean)
               3.gender        =    .0332963 (mean)
               1.statuscat     =    .2241953 (mean)
               2.statuscat     =    .3174251 (mean)
               3.statuscat     =    .4583796 (mean)
               age             =    35.02415 (mean)
               1.culture       =    .1664817 (mean)
               2.culture       =    .3429523 (mean)
               3.culture       =     .490566 (mean)
               1.continent     =    .4827969 (mean)
               2.continent     =    .5172031 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.rand2      |  (base outcome)
-------------+----------------------------------------------------------------
2.rand2      |
         _at |
          1  |  -.2125398   .0267799    -7.94   0.000    -.2650275   -.1600521
          2  |  -.2461425   .0797227    -3.09   0.002    -.4023961   -.0898889
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, scheme(plottig) xline(0, lpattern(dash) lcolor(black)) title(Confirmatory, size(small)) coeflabels(1._at = "No" 2._at = "Yes") xscale(range(-0.8 0.8)) xlabel(-0.8(0.1)0.8) nodraw mcolor(bl
> ack) mfcolor(black) msymbol(diamond)  ciopts(lcolor(black))
(note:  alignstroke foreground not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke tick not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke grid not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke major_grid not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke minortick not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke axisline not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke background not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke plotregion not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1mark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1 not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1other not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke dotmark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  anglestyle symbol not found in scheme, default attributes used)
(note:  anglestyle symbol not found in scheme, default attributes used)
(note:  alignstroke p2mark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p2 not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p2other not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke xyline not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)

. graph save Graph "output/figA8m2.gph", replace
(file output/figA8m2.gph saved)

. mlogit vig2outcome i.Manipulate1n#i.rand2 i.gender i.statuscat age i.culture i.continent, robust cluster(country)

Iteration 0:   log pseudolikelihood = -953.30846  
Iteration 1:   log pseudolikelihood = -782.23837  
Iteration 2:   log pseudolikelihood =  -780.5249  
Iteration 3:   log pseudolikelihood =  -780.4886  
Iteration 4:   log pseudolikelihood = -780.48843  
Iteration 5:   log pseudolikelihood = -780.48843  

Multinomial logistic regression                 Number of obs     =        901
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -780.48843               Pseudo R2         =     0.1813

                                               (Std. Err. adjusted for 11 clusters in country)
----------------------------------------------------------------------------------------------
                             |               Robust
                 vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
exploratory                  |  (base outcome)
-----------------------------+----------------------------------------------------------------
confirmatory                 |
          Manipulate1n#rand2 |
 No#not extensively studied  |  -2.283806   .1682152   -13.58   0.000    -2.613502   -1.954111
    Yes#extensively studied  |    .225593   .3297945     0.68   0.494    -.4207924    .8719784
Yes#not extensively studied  |  -2.134446   .4075888    -5.24   0.000    -2.933305   -1.335587
                             |
                      gender |
                       Male  |   .3054878    .199245     1.53   0.125    -.0850252    .6960007
       Prefer not to answer  |   .1642675   .4873865     0.34   0.736    -.7909924    1.119527
                             |
                   statuscat |
             Senior Scholar  |    .205336   .6005476     0.34   0.732    -.9717156    1.382388
             Junior Scholar  |  -.2373976     .86779    -0.27   0.784    -1.938235    1.463439
                             |
                         age |  -.0088562   .0255347    -0.35   0.729    -.0589033     .041191
                             |
                     culture |
               Quantitative  |    1.63364   .3634592     4.49   0.000     .9212732    2.346007
               Mixed-method  |    1.21857   .3224883     3.78   0.000     .5865046    1.850636
                             |
                   continent |
                         US  |  -.1992404   .1851546    -1.08   0.282    -.5621368    .1636561
                       _cons |  -.4736966   1.597426    -0.30   0.767    -3.604593      2.6572
-----------------------------+----------------------------------------------------------------
process                      |
          Manipulate1n#rand2 |
 No#not extensively studied  |  -2.519454   .3270133    -7.70   0.000    -3.160389    -1.87852
    Yes#extensively studied  |   -.075566   .3789556    -0.20   0.842    -.8183054    .6671735
Yes#not extensively studied  |  -3.839903   1.217101    -3.15   0.002    -6.225376    -1.45443
                             |
                      gender |
                       Male  |   .0245825   .1377532     0.18   0.858    -.2454087    .2945738
       Prefer not to answer  |     .15981   .5644893     0.28   0.777    -.9465688    1.266189
                             |
                   statuscat |
             Senior Scholar  |   .5366814   .6331684     0.85   0.397    -.7043058    1.777669
             Junior Scholar  |   .6002698   .7440214     0.81   0.420    -.8579852    2.058525
                             |
                         age |   .0190761   .0218905     0.87   0.384    -.0238285    .0619806
                             |
                     culture |
               Quantitative  |  -.4053594   .2846321    -1.42   0.154     -.963228    .1525093
               Mixed-method  |  -.0370435   .2649849    -0.14   0.889    -.5564042    .4823173
                             |
                   continent |
                         US  |  -.3897497   .1522433    -2.56   0.010    -.6881411   -.0913582
                       _cons |  -.0287149   1.549744    -0.02   0.985    -3.066158    3.008728
----------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2) at(Manipulate1n=(0 1)) pr(out(3)) post atmeans

Conditional marginal effects                    Number of obs     =        901
Model VCE    : Robust

Expression   : Pr(vig2outcome==process), predict(out(3))
dy/dx w.r.t. : 2.rand2

1._at        : Manipulate1n    =           0
               1.rand2         =     .509434 (mean)
               2.rand2         =     .490566 (mean)
               1.gender        =    .3240844 (mean)
               2.gender        =    .6426193 (mean)
               3.gender        =    .0332963 (mean)
               1.statuscat     =    .2241953 (mean)
               2.statuscat     =    .3174251 (mean)
               3.statuscat     =    .4583796 (mean)
               age             =    35.02415 (mean)
               1.culture       =    .1664817 (mean)
               2.culture       =    .3429523 (mean)
               3.culture       =     .490566 (mean)
               1.continent     =    .4827969 (mean)
               2.continent     =    .5172031 (mean)

2._at        : Manipulate1n    =           1
               1.rand2         =     .509434 (mean)
               2.rand2         =     .490566 (mean)
               1.gender        =    .3240844 (mean)
               2.gender        =    .6426193 (mean)
               3.gender        =    .0332963 (mean)
               1.statuscat     =    .2241953 (mean)
               2.statuscat     =    .3174251 (mean)
               3.statuscat     =    .4583796 (mean)
               age             =    35.02415 (mean)
               1.culture       =    .1664817 (mean)
               2.culture       =    .3429523 (mean)
               3.culture       =     .490566 (mean)
               1.continent     =    .4827969 (mean)
               2.continent     =    .5172031 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.rand2      |  (base outcome)
-------------+----------------------------------------------------------------
2.rand2      |
         _at |
          1  |  -.3261555   .0478811    -6.81   0.000    -.4200007   -.2323104
          2  |  -.3639564   .0990555    -3.67   0.000    -.5581016   -.1698112
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, scheme(plottig) xline(0, lpattern(dash) lcolor(black)) title(Process, size(small)) coeflabels(1._at = "No" 2._at = "Yes") xscale(range(-0.8 0.8)) xlabel(-0.8(0.1)0.8) nodraw mcolor(black) 
> mfcolor(black) msymbol(diamond)  ciopts(lcolor(black))
(note:  alignstroke foreground not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke tick not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke grid not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke major_grid not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke minortick not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke axisline not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke background not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke plotregion not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1mark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1 not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1other not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke dotmark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  anglestyle symbol not found in scheme, default attributes used)
(note:  anglestyle symbol not found in scheme, default attributes used)
(note:  alignstroke p2mark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p2 not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p2other not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke xyline not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)

. graph save Graph "output/figA8m3.gph", replace
(file output/figA8m3.gph saved)

. graph combine "output/figA8m1.gph" "output/figA8m2.gph" "output/figA8m3.gph", rows(3) imargin(small) title("") commonscheme graphregion(color(white) margin(small) fcolor(white)) ycommon

. graph save Graph "output/figA8.gph", replace
(file output/figA8.gph saved)

. graph export "output/figureA8.tif", width(1000) replace
(file output/figureA8.tif written in TIFF format)

. est clear

. 
. *** Replication of main and interaction effects with alternative coding of research cultures
. use "data/fulldata.dta", clear

. keep if lastpage==17 
(679 observations deleted)

. keep if attention1=="Yes" & attention2=="Yes" & attention3=="Yes" & attention4=="No" & attention5=="No"
(79 observations deleted)

. gen culture2=culture
(66 missing values generated)

. count if methodcat==1 & numberofcases==2 | methodcat==2 & numberofcases==1
  28

. replace culture2=3 if methodcat==1 & numberofcases==2 | methodcat==2 & numberofcases==1
(28 real changes made)

. label values culture2 culture

. 
. *Define sample 
. mlogit vig2outcome i.rand2 i.culture2 i.gender i.statuscat age i.continent, robust cluster(country)

Iteration 0:   log pseudolikelihood = -983.65333  
Iteration 1:   log pseudolikelihood = -807.95126  
Iteration 2:   log pseudolikelihood = -806.80313  
Iteration 3:   log pseudolikelihood = -806.79926  
Iteration 4:   log pseudolikelihood = -806.79926  

Multinomial logistic regression                 Number of obs     =        929
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -806.79926               Pseudo R2         =     0.1798

                                           (Std. Err. adjusted for 11 clusters in country)
------------------------------------------------------------------------------------------
                         |               Robust
             vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
exploratory              |  (base outcome)
-------------------------+----------------------------------------------------------------
confirmatory             |
                   rand2 |
not extensively studied  |  -2.248466    .169915   -13.23   0.000    -2.581494   -1.915439
                         |
                culture2 |
           Quantitative  |   1.615289   .3597617     4.49   0.000     .9101691    2.320409
           Mixed-method  |   1.225373   .3200441     3.83   0.000     .5980976    1.852648
                         |
                  gender |
                   Male  |   .3597876   .1794125     2.01   0.045     .0081456    .7114296
   Prefer not to answer  |   .1220609   .5184916     0.24   0.814     -.894164    1.138286
                         |
               statuscat |
         Senior Scholar  |   .1874917   .5822914     0.32   0.747    -.9537785    1.328762
         Junior Scholar  |   -.268936   .8374637    -0.32   0.748    -1.910335    1.372463
                         |
                     age |  -.0101258   .0242584    -0.42   0.676    -.0576713    .0374197
                         |
               continent |
                     US  |  -.1874638   .1731251    -1.08   0.279    -.5267827    .1518551
                   _cons |   -.429542   1.538705    -0.28   0.780    -3.445348    2.586265
-------------------------+----------------------------------------------------------------
process                  |
                   rand2 |
not extensively studied  |   -2.61322   .3326402    -7.86   0.000    -3.265183   -1.961257
                         |
                culture2 |
           Quantitative  |  -.4409328   .2746284    -1.61   0.108    -.9791945    .0973289
           Mixed-method  |  -.0433532   .2448747    -0.18   0.859    -.5232988    .4365925
                         |
                  gender |
                   Male  |   .0753637   .1356116     0.56   0.578    -.1904302    .3411577
   Prefer not to answer  |   .1478206   .5808279     0.25   0.799    -.9905812    1.286222
                         |
               statuscat |
         Senior Scholar  |    .514447   .5958111     0.86   0.388    -.6533212    1.682215
         Junior Scholar  |   .5512524   .7057237     0.78   0.435    -.8319408    1.934445
                         |
                     age |   .0185678   .0225009     0.83   0.409    -.0255332    .0626689
                         |
               continent |
                     US  |  -.3499297   .1515979    -2.31   0.021    -.6470561   -.0528033
                   _cons |  -.0071891   1.508238    -0.00   0.996    -2.963281    2.948902
------------------------------------------------------------------------------------------

. keep if e(sample)
(39 observations deleted)

. 
. *Figure A9: Main Effects with alternative coding of research culture
. mlogit vig2outcome i.rand2 i.culture2 i.gender i.statuscat age i.continent, robust cluster(country)

Iteration 0:   log pseudolikelihood = -983.65333  
Iteration 1:   log pseudolikelihood = -807.95126  
Iteration 2:   log pseudolikelihood = -806.80313  
Iteration 3:   log pseudolikelihood = -806.79926  
Iteration 4:   log pseudolikelihood = -806.79926  

Multinomial logistic regression                 Number of obs     =        929
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -806.79926               Pseudo R2         =     0.1798

                                           (Std. Err. adjusted for 11 clusters in country)
------------------------------------------------------------------------------------------
                         |               Robust
             vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
exploratory              |  (base outcome)
-------------------------+----------------------------------------------------------------
confirmatory             |
                   rand2 |
not extensively studied  |  -2.248466    .169915   -13.23   0.000    -2.581494   -1.915439
                         |
                culture2 |
           Quantitative  |   1.615289   .3597617     4.49   0.000     .9101691    2.320409
           Mixed-method  |   1.225373   .3200441     3.83   0.000     .5980976    1.852648
                         |
                  gender |
                   Male  |   .3597876   .1794125     2.01   0.045     .0081456    .7114296
   Prefer not to answer  |   .1220609   .5184916     0.24   0.814     -.894164    1.138286
                         |
               statuscat |
         Senior Scholar  |   .1874917   .5822914     0.32   0.747    -.9537785    1.328762
         Junior Scholar  |   -.268936   .8374637    -0.32   0.748    -1.910335    1.372463
                         |
                     age |  -.0101258   .0242584    -0.42   0.676    -.0576713    .0374197
                         |
               continent |
                     US  |  -.1874638   .1731251    -1.08   0.279    -.5267827    .1518551
                   _cons |   -.429542   1.538705    -0.28   0.780    -3.445348    2.586265
-------------------------+----------------------------------------------------------------
process                  |
                   rand2 |
not extensively studied  |   -2.61322   .3326402    -7.86   0.000    -3.265183   -1.961257
                         |
                culture2 |
           Quantitative  |  -.4409328   .2746284    -1.61   0.108    -.9791945    .0973289
           Mixed-method  |  -.0433532   .2448747    -0.18   0.859    -.5232988    .4365925
                         |
                  gender |
                   Male  |   .0753637   .1356116     0.56   0.578    -.1904302    .3411577
   Prefer not to answer  |   .1478206   .5808279     0.25   0.799    -.9905812    1.286222
                         |
               statuscat |
         Senior Scholar  |    .514447   .5958111     0.86   0.388    -.6533212    1.682215
         Junior Scholar  |   .5512524   .7057237     0.78   0.435    -.8319408    1.934445
                         |
                     age |   .0185678   .0225009     0.83   0.409    -.0255332    .0626689
                         |
               continent |
                     US  |  -.3499297   .1515979    -2.31   0.021    -.6470561   -.0528033
                   _cons |  -.0071891   1.508238    -0.00   0.996    -2.963281    2.948902
------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2 culture2) pr(out(1)) post

Average marginal effects                        Number of obs     =        929
Model VCE    : Robust

Expression   : Pr(vig2outcome==exploratory), predict(out(1))
dy/dx w.r.t. : 2.rand2 2.culture2 3.culture2

------------------------------------------------------------------------------------------
                         |            Delta-method
                         |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                   rand2 |
not extensively studied  |   .5361431   .0372166    14.41   0.000        .4632    .6090863
                         |
                culture2 |
           Quantitative  |  -.0679599    .043969    -1.55   0.122    -.1541376    .0182177
           Mixed-method  |  -.0612847   .0384484    -1.59   0.111    -.1366422    .0140728
------------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, xline(0, lpattern(dash) lcolor(black)) title(Exploratory, size(small)) xscale(range(0.6)) xlabel(-0.6(0.1)0.6) nodraw mcolor(black) mfcolor(black) msymbol(diamond)  ciopts(lcolor(black))

. graph save Graph "output/figA9m1.gph", replace
(file output/figA9m1.gph saved)

. mlogit vig2outcome i.rand2 i.culture2 i.gender i.statuscat age i.continent, robust cluster(country)

Iteration 0:   log pseudolikelihood = -983.65333  
Iteration 1:   log pseudolikelihood = -807.95126  
Iteration 2:   log pseudolikelihood = -806.80313  
Iteration 3:   log pseudolikelihood = -806.79926  
Iteration 4:   log pseudolikelihood = -806.79926  

Multinomial logistic regression                 Number of obs     =        929
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -806.79926               Pseudo R2         =     0.1798

                                           (Std. Err. adjusted for 11 clusters in country)
------------------------------------------------------------------------------------------
                         |               Robust
             vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
exploratory              |  (base outcome)
-------------------------+----------------------------------------------------------------
confirmatory             |
                   rand2 |
not extensively studied  |  -2.248466    .169915   -13.23   0.000    -2.581494   -1.915439
                         |
                culture2 |
           Quantitative  |   1.615289   .3597617     4.49   0.000     .9101691    2.320409
           Mixed-method  |   1.225373   .3200441     3.83   0.000     .5980976    1.852648
                         |
                  gender |
                   Male  |   .3597876   .1794125     2.01   0.045     .0081456    .7114296
   Prefer not to answer  |   .1220609   .5184916     0.24   0.814     -.894164    1.138286
                         |
               statuscat |
         Senior Scholar  |   .1874917   .5822914     0.32   0.747    -.9537785    1.328762
         Junior Scholar  |   -.268936   .8374637    -0.32   0.748    -1.910335    1.372463
                         |
                     age |  -.0101258   .0242584    -0.42   0.676    -.0576713    .0374197
                         |
               continent |
                     US  |  -.1874638   .1731251    -1.08   0.279    -.5267827    .1518551
                   _cons |   -.429542   1.538705    -0.28   0.780    -3.445348    2.586265
-------------------------+----------------------------------------------------------------
process                  |
                   rand2 |
not extensively studied  |   -2.61322   .3326402    -7.86   0.000    -3.265183   -1.961257
                         |
                culture2 |
           Quantitative  |  -.4409328   .2746284    -1.61   0.108    -.9791945    .0973289
           Mixed-method  |  -.0433532   .2448747    -0.18   0.859    -.5232988    .4365925
                         |
                  gender |
                   Male  |   .0753637   .1356116     0.56   0.578    -.1904302    .3411577
   Prefer not to answer  |   .1478206   .5808279     0.25   0.799    -.9905812    1.286222
                         |
               statuscat |
         Senior Scholar  |    .514447   .5958111     0.86   0.388    -.6533212    1.682215
         Junior Scholar  |   .5512524   .7057237     0.78   0.435    -.8319408    1.934445
                         |
                     age |   .0185678   .0225009     0.83   0.409    -.0255332    .0626689
                         |
               continent |
                     US  |  -.3499297   .1515979    -2.31   0.021    -.6470561   -.0528033
                   _cons |  -.0071891   1.508238    -0.00   0.996    -2.963281    2.948902
------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2 culture2) pr(out(2)) post

Average marginal effects                        Number of obs     =        929
Model VCE    : Robust

Expression   : Pr(vig2outcome==confirmatory), predict(out(2))
dy/dx w.r.t. : 2.rand2 2.culture2 3.culture2

------------------------------------------------------------------------------------------
                         |            Delta-method
                         |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                   rand2 |
not extensively studied  |  -.2100649   .0200329   -10.49   0.000    -.2493287    -.170801
                         |
                culture2 |
           Quantitative  |   .2558661    .039984     6.40   0.000     .1774989    .3342334
           Mixed-method  |   .1485653   .0313018     4.75   0.000     .0872148    .2099157
------------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, scheme(s1mono) xline(0, lpattern(dash) lcolor(black)) title(Confirmatory, size(small)) xscale(range(0.6)) xlabel(-0.6(0.1)0.6) nodraw mcolor(black) mfcolor(black) msymbol(diamond) ciopts(l
> color(black))

. graph save Graph "output/figA9m2.gph", replace
(file output/figA9m2.gph saved)

. mlogit vig2outcome i.rand2 i.culture2 i.gender i.statuscat age i.continent, robust cluster(country)

Iteration 0:   log pseudolikelihood = -983.65333  
Iteration 1:   log pseudolikelihood = -807.95126  
Iteration 2:   log pseudolikelihood = -806.80313  
Iteration 3:   log pseudolikelihood = -806.79926  
Iteration 4:   log pseudolikelihood = -806.79926  

Multinomial logistic regression                 Number of obs     =        929
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -806.79926               Pseudo R2         =     0.1798

                                           (Std. Err. adjusted for 11 clusters in country)
------------------------------------------------------------------------------------------
                         |               Robust
             vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
exploratory              |  (base outcome)
-------------------------+----------------------------------------------------------------
confirmatory             |
                   rand2 |
not extensively studied  |  -2.248466    .169915   -13.23   0.000    -2.581494   -1.915439
                         |
                culture2 |
           Quantitative  |   1.615289   .3597617     4.49   0.000     .9101691    2.320409
           Mixed-method  |   1.225373   .3200441     3.83   0.000     .5980976    1.852648
                         |
                  gender |
                   Male  |   .3597876   .1794125     2.01   0.045     .0081456    .7114296
   Prefer not to answer  |   .1220609   .5184916     0.24   0.814     -.894164    1.138286
                         |
               statuscat |
         Senior Scholar  |   .1874917   .5822914     0.32   0.747    -.9537785    1.328762
         Junior Scholar  |   -.268936   .8374637    -0.32   0.748    -1.910335    1.372463
                         |
                     age |  -.0101258   .0242584    -0.42   0.676    -.0576713    .0374197
                         |
               continent |
                     US  |  -.1874638   .1731251    -1.08   0.279    -.5267827    .1518551
                   _cons |   -.429542   1.538705    -0.28   0.780    -3.445348    2.586265
-------------------------+----------------------------------------------------------------
process                  |
                   rand2 |
not extensively studied  |   -2.61322   .3326402    -7.86   0.000    -3.265183   -1.961257
                         |
                culture2 |
           Quantitative  |  -.4409328   .2746284    -1.61   0.108    -.9791945    .0973289
           Mixed-method  |  -.0433532   .2448747    -0.18   0.859    -.5232988    .4365925
                         |
                  gender |
                   Male  |   .0753637   .1356116     0.56   0.578    -.1904302    .3411577
   Prefer not to answer  |   .1478206   .5808279     0.25   0.799    -.9905812    1.286222
                         |
               statuscat |
         Senior Scholar  |    .514447   .5958111     0.86   0.388    -.6533212    1.682215
         Junior Scholar  |   .5512524   .7057237     0.78   0.435    -.8319408    1.934445
                         |
                     age |   .0185678   .0225009     0.83   0.409    -.0255332    .0626689
                         |
               continent |
                     US  |  -.3499297   .1515979    -2.31   0.021    -.6470561   -.0528033
                   _cons |  -.0071891   1.508238    -0.00   0.996    -2.963281    2.948902
------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2 culture2) pr(out(3)) post

Average marginal effects                        Number of obs     =        929
Model VCE    : Robust

Expression   : Pr(vig2outcome==process), predict(out(3))
dy/dx w.r.t. : 2.rand2 2.culture2 3.culture2

------------------------------------------------------------------------------------------
                         |            Delta-method
                         |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                   rand2 |
not extensively studied  |  -.3260783   .0413888    -7.88   0.000    -.4071989   -.2449577
                         |
                culture2 |
           Quantitative  |  -.1879062   .0522297    -3.60   0.000    -.2902745    -.085538
           Mixed-method  |  -.0872806   .0470604    -1.85   0.064    -.1795173    .0049561
------------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, scheme(s1mono) xline(0, lpattern(dash) lcolor(black)) title(Process, size(small)) xscale(range(0.6)) xlabel(-0.6(0.1)0.6) nodraw mcolor(black) mfcolor(black) msymbol(diamond) ciopts(lcolor
> (black))

. graph save Graph "output/figA9m3.gph", replace
(file output/figA9m3.gph saved)

. graph combine "output/figA9m1.gph" "output/figA9m2.gph" "output/figA9m3.gph", rows(3) imargin(small) title("") commonscheme graphregion(color(white) margin(small) fcolor(white)) xcommon ycommon

. graph save Graph "output/figA9.gph", replace
(file output/figA9.gph saved)

. graph export "output/figureA9.tif", width(1000) replace
(file output/figureA9.tif written in TIFF format)

. est clear

. 
. *Figure A10: Treatment effect accross research cultures with alternative coding of research culture
. mlogit vig2outcome i.culture2#i.rand2 i.gender i.statuscat age i.continent, robust cluster(country)

Iteration 0:   log pseudolikelihood = -983.65333  
Iteration 1:   log pseudolikelihood = -812.36664  
Iteration 2:   log pseudolikelihood = -805.44736  
Iteration 3:   log pseudolikelihood = -805.22974  
Iteration 4:   log pseudolikelihood = -805.22867  
Iteration 5:   log pseudolikelihood = -805.22867  

Multinomial logistic regression                 Number of obs     =        929
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -805.22867               Pseudo R2         =     0.1814

                                                        (Std. Err. adjusted for 11 clusters in country)
-------------------------------------------------------------------------------------------------------
                                      |               Robust
                          vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------------------------+----------------------------------------------------------------
exploratory                           |  (base outcome)
--------------------------------------+----------------------------------------------------------------
confirmatory                          |
                       culture2#rand2 |
 Qualitative#not extensively studied  |  -2.839378    1.21571    -2.34   0.020    -5.222126   -.4566291
    Quantitative#extensively studied  |   1.432787   .5681335     2.52   0.012     .3192661    2.546308
Quantitative#not extensively studied  |  -.5783869    .606577    -0.95   0.340    -1.767256    .6104823
    Mixed-method#extensively studied  |   1.227623   .6677174     1.84   0.066    -.0810789    2.536325
Mixed-method#not extensively studied  |    -1.2036    .576943    -2.09   0.037    -2.334387   -.0728123
                                      |
                               gender |
                                Male  |   .3665223   .1789449     2.05   0.041     .0157967    .7172479
                Prefer not to answer  |   .1666586   .5157166     0.32   0.747    -.8441274    1.177445
                                      |
                            statuscat |
                      Senior Scholar  |   .2003526   .5851661     0.34   0.732    -.9465518    1.347257
                      Junior Scholar  |  -.2440452   .8304683    -0.29   0.769    -1.871733    1.383643
                                      |
                                  age |  -.0092697   .0242398    -0.38   0.702    -.0567788    .0382393
                                      |
                            continent |
                                  US  |  -.1854459   .1718348    -1.08   0.280    -.5222359    .1513442
                                _cons |    -.39575   1.793419    -0.22   0.825    -3.910786    3.119286
--------------------------------------+----------------------------------------------------------------
process                               |
                       culture2#rand2 |
 Qualitative#not extensively studied  |  -2.309943   .3926304    -5.88   0.000    -3.079485   -1.540402
    Quantitative#extensively studied  |  -.3294127   .5001809    -0.66   0.510    -1.309749    .6509239
Quantitative#not extensively studied  |   -3.10705    .475621    -6.53   0.000     -4.03925    -2.17485
    Mixed-method#extensively studied  |   .1389278    .474407     0.29   0.770    -.7908928    1.068748
Mixed-method#not extensively studied  |  -2.563082   .4762849    -5.38   0.000    -3.496583   -1.629581
                                      |
                               gender |
                                Male  |    .079302    .132143     0.60   0.548    -.1796936    .3382975
                Prefer not to answer  |   .1593184    .570028     0.28   0.780     -.957916    1.276553
                                      |
                            statuscat |
                      Senior Scholar  |    .506112   .5799479     0.87   0.383    -.6305649    1.642789
                      Junior Scholar  |    .528801   .6757877     0.78   0.434    -.7957185     1.85332
                                      |
                                  age |   .0176965   .0212586     0.83   0.405    -.0239695    .0593625
                                      |
                            continent |
                                  US  |  -.3528767   .1498959    -2.35   0.019    -.6466672   -.0590862
                                _cons |  -.0852636   1.625893    -0.05   0.958    -3.271955    3.101427
-------------------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2) at(culture2=(1 2 3)) pr(out(1)) post

Average marginal effects                        Number of obs     =        929
Model VCE    : Robust

Expression   : Pr(vig2outcome==exploratory), predict(out(1))
dy/dx w.r.t. : 2.rand2

1._at        : culture2        =           1

2._at        : culture2        =           2

3._at        : culture2        =           3

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.rand2      |  (base outcome)
-------------+----------------------------------------------------------------
2.rand2      |
         _at |
          1  |    .528389   .0874385     6.04   0.000     .3570127    .6997652
          2  |    .503024   .0488301    10.30   0.000     .4073188    .5987292
          3  |    .559728    .052913    10.58   0.000     .4560205    .6634355
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, xline(0, lpattern(dash) lcolor(black)) title(Exploratory, size(small)) coeflabels(1._at = "Qualitative" 2._at = "Quantitative"  3._at = "Mixed-Methods") xscale(range(-0.7 0.7)) xlabel(-0.7
> (0.1)0.7) nodraw mcolor(black) mfcolor(black) msymbol(diamond)  ciopts(lcolor(black))

. graph save Graph "output/figA10m1.gph", replace
(file output/figA10m1.gph saved)

. est clear

. mlogit vig2outcome i.culture2#i.rand2 i.gender i.statuscat age i.continent, robust cluster(country)

Iteration 0:   log pseudolikelihood = -983.65333  
Iteration 1:   log pseudolikelihood = -812.36664  
Iteration 2:   log pseudolikelihood = -805.44736  
Iteration 3:   log pseudolikelihood = -805.22974  
Iteration 4:   log pseudolikelihood = -805.22867  
Iteration 5:   log pseudolikelihood = -805.22867  

Multinomial logistic regression                 Number of obs     =        929
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -805.22867               Pseudo R2         =     0.1814

                                                        (Std. Err. adjusted for 11 clusters in country)
-------------------------------------------------------------------------------------------------------
                                      |               Robust
                          vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------------------------+----------------------------------------------------------------
exploratory                           |  (base outcome)
--------------------------------------+----------------------------------------------------------------
confirmatory                          |
                       culture2#rand2 |
 Qualitative#not extensively studied  |  -2.839378    1.21571    -2.34   0.020    -5.222126   -.4566291
    Quantitative#extensively studied  |   1.432787   .5681335     2.52   0.012     .3192661    2.546308
Quantitative#not extensively studied  |  -.5783869    .606577    -0.95   0.340    -1.767256    .6104823
    Mixed-method#extensively studied  |   1.227623   .6677174     1.84   0.066    -.0810789    2.536325
Mixed-method#not extensively studied  |    -1.2036    .576943    -2.09   0.037    -2.334387   -.0728123
                                      |
                               gender |
                                Male  |   .3665223   .1789449     2.05   0.041     .0157967    .7172479
                Prefer not to answer  |   .1666586   .5157166     0.32   0.747    -.8441274    1.177445
                                      |
                            statuscat |
                      Senior Scholar  |   .2003526   .5851661     0.34   0.732    -.9465518    1.347257
                      Junior Scholar  |  -.2440452   .8304683    -0.29   0.769    -1.871733    1.383643
                                      |
                                  age |  -.0092697   .0242398    -0.38   0.702    -.0567788    .0382393
                                      |
                            continent |
                                  US  |  -.1854459   .1718348    -1.08   0.280    -.5222359    .1513442
                                _cons |    -.39575   1.793419    -0.22   0.825    -3.910786    3.119286
--------------------------------------+----------------------------------------------------------------
process                               |
                       culture2#rand2 |
 Qualitative#not extensively studied  |  -2.309943   .3926304    -5.88   0.000    -3.079485   -1.540402
    Quantitative#extensively studied  |  -.3294127   .5001809    -0.66   0.510    -1.309749    .6509239
Quantitative#not extensively studied  |   -3.10705    .475621    -6.53   0.000     -4.03925    -2.17485
    Mixed-method#extensively studied  |   .1389278    .474407     0.29   0.770    -.7908928    1.068748
Mixed-method#not extensively studied  |  -2.563082   .4762849    -5.38   0.000    -3.496583   -1.629581
                                      |
                               gender |
                                Male  |    .079302    .132143     0.60   0.548    -.1796936    .3382975
                Prefer not to answer  |   .1593184    .570028     0.28   0.780     -.957916    1.276553
                                      |
                            statuscat |
                      Senior Scholar  |    .506112   .5799479     0.87   0.383    -.6305649    1.642789
                      Junior Scholar  |    .528801   .6757877     0.78   0.434    -.7957185     1.85332
                                      |
                                  age |   .0176965   .0212586     0.83   0.405    -.0239695    .0593625
                                      |
                            continent |
                                  US  |  -.3528767   .1498959    -2.35   0.019    -.6466672   -.0590862
                                _cons |  -.0852636   1.625893    -0.05   0.958    -3.271955    3.101427
-------------------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2) at(culture2=(1 2 3)) pr(out(2)) post

Average marginal effects                        Number of obs     =        929
Model VCE    : Robust

Expression   : Pr(vig2outcome==confirmatory), predict(out(2))
dy/dx w.r.t. : 2.rand2

1._at        : culture2        =           1

2._at        : culture2        =           2

3._at        : culture2        =           3

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.rand2      |  (base outcome)
-------------+----------------------------------------------------------------
2.rand2      |
         _at |
          1  |  -.1177002   .0639053    -1.84   0.066    -.2429523    .0075519
          2  |  -.2430476   .0492533    -4.93   0.000    -.3395823   -.1465129
          3  |  -.2157448   .0264629    -8.15   0.000    -.2676111   -.1638784
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, xline(0, lpattern(dash) lcolor(black)) title(Confirmatory, size(small)) coeflabels(1._at = "Qualitative" 2._at = "Quantitative"  3._at = "Mixed-Methods") xscale(range(-0.7 0.7)) xlabel(-0.
> 7(0.1)0.7) nodraw mcolor(black) mfcolor(black) msymbol(diamond)  ciopts(lcolor(black))

. graph save Graph "output/figA10m2.gph", replace
(file output/figA10m2.gph saved)

. est clear

. mlogit vig2outcome i.culture2#i.rand2 i.gender i.statuscat age i.continent, robust cluster(country)

Iteration 0:   log pseudolikelihood = -983.65333  
Iteration 1:   log pseudolikelihood = -812.36664  
Iteration 2:   log pseudolikelihood = -805.44736  
Iteration 3:   log pseudolikelihood = -805.22974  
Iteration 4:   log pseudolikelihood = -805.22867  
Iteration 5:   log pseudolikelihood = -805.22867  

Multinomial logistic regression                 Number of obs     =        929
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -805.22867               Pseudo R2         =     0.1814

                                                        (Std. Err. adjusted for 11 clusters in country)
-------------------------------------------------------------------------------------------------------
                                      |               Robust
                          vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------------------------+----------------------------------------------------------------
exploratory                           |  (base outcome)
--------------------------------------+----------------------------------------------------------------
confirmatory                          |
                       culture2#rand2 |
 Qualitative#not extensively studied  |  -2.839378    1.21571    -2.34   0.020    -5.222126   -.4566291
    Quantitative#extensively studied  |   1.432787   .5681335     2.52   0.012     .3192661    2.546308
Quantitative#not extensively studied  |  -.5783869    .606577    -0.95   0.340    -1.767256    .6104823
    Mixed-method#extensively studied  |   1.227623   .6677174     1.84   0.066    -.0810789    2.536325
Mixed-method#not extensively studied  |    -1.2036    .576943    -2.09   0.037    -2.334387   -.0728123
                                      |
                               gender |
                                Male  |   .3665223   .1789449     2.05   0.041     .0157967    .7172479
                Prefer not to answer  |   .1666586   .5157166     0.32   0.747    -.8441274    1.177445
                                      |
                            statuscat |
                      Senior Scholar  |   .2003526   .5851661     0.34   0.732    -.9465518    1.347257
                      Junior Scholar  |  -.2440452   .8304683    -0.29   0.769    -1.871733    1.383643
                                      |
                                  age |  -.0092697   .0242398    -0.38   0.702    -.0567788    .0382393
                                      |
                            continent |
                                  US  |  -.1854459   .1718348    -1.08   0.280    -.5222359    .1513442
                                _cons |    -.39575   1.793419    -0.22   0.825    -3.910786    3.119286
--------------------------------------+----------------------------------------------------------------
process                               |
                       culture2#rand2 |
 Qualitative#not extensively studied  |  -2.309943   .3926304    -5.88   0.000    -3.079485   -1.540402
    Quantitative#extensively studied  |  -.3294127   .5001809    -0.66   0.510    -1.309749    .6509239
Quantitative#not extensively studied  |   -3.10705    .475621    -6.53   0.000     -4.03925    -2.17485
    Mixed-method#extensively studied  |   .1389278    .474407     0.29   0.770    -.7908928    1.068748
Mixed-method#not extensively studied  |  -2.563082   .4762849    -5.38   0.000    -3.496583   -1.629581
                                      |
                               gender |
                                Male  |    .079302    .132143     0.60   0.548    -.1796936    .3382975
                Prefer not to answer  |   .1593184    .570028     0.28   0.780     -.957916    1.276553
                                      |
                            statuscat |
                      Senior Scholar  |    .506112   .5799479     0.87   0.383    -.6305649    1.642789
                      Junior Scholar  |    .528801   .6757877     0.78   0.434    -.7957185     1.85332
                                      |
                                  age |   .0176965   .0212586     0.83   0.405    -.0239695    .0593625
                                      |
                            continent |
                                  US  |  -.3528767   .1498959    -2.35   0.019    -.6466672   -.0590862
                                _cons |  -.0852636   1.625893    -0.05   0.958    -3.271955    3.101427
-------------------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2) at(culture2=(1 2 3)) pr(out(3)) post

Average marginal effects                        Number of obs     =        929
Model VCE    : Robust

Expression   : Pr(vig2outcome==process), predict(out(3))
dy/dx w.r.t. : 2.rand2

1._at        : culture2        =           1

2._at        : culture2        =           2

3._at        : culture2        =           3

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.rand2      |  (base outcome)
-------------+----------------------------------------------------------------
2.rand2      |
         _at |
          1  |  -.4106888   .0824164    -4.98   0.000     -.572222   -.2491556
          2  |  -.2599764   .0416788    -6.24   0.000    -.3416653   -.1782875
          3  |  -.3439832   .0504522    -6.82   0.000    -.4428677   -.2450987
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, xline(0, lpattern(dash) lcolor(black)) title(Process, size(small)) coeflabels(1._at = "Qualitative" 2._at = "Quantitative"  3._at = "Mixed-Methods") xscale(range(-0.7 0.7)) xlabel(-0.7(0.1
> )0.7) nodraw mcolor(black) mfcolor(black) msymbol(diamond)  ciopts(lcolor(black))

. graph save Graph "output/figA10m3.gph", replace
(file output/figA10m3.gph saved)

. est clear

. graph combine "output/figA10m1.gph" "output/figA10m2.gph" "output/figA10m3.gph", rows(3) imargin(small) title("") commonscheme graphregion(color(white) margin(small) fcolor(white)) ycommon

. graph save Graph "output/figA10.gph", replace
(file output/figA10.gph saved)

. graph export "output/figureA10.tif", width(1000) replace
(file output/figureA10.tif written in TIFF format)

. est clear

. 
. *Figure A11: Probability of choosing a specific goal of inference across research cultures and treatment conditions with alternative coding of research culture
. mlogit vig2outcome i.culture2#i.rand2 i.gender i.statuscat age i.continent, robust cluster(country)

Iteration 0:   log pseudolikelihood = -983.65333  
Iteration 1:   log pseudolikelihood = -812.36664  
Iteration 2:   log pseudolikelihood = -805.44736  
Iteration 3:   log pseudolikelihood = -805.22974  
Iteration 4:   log pseudolikelihood = -805.22867  
Iteration 5:   log pseudolikelihood = -805.22867  

Multinomial logistic regression                 Number of obs     =        929
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -805.22867               Pseudo R2         =     0.1814

                                                        (Std. Err. adjusted for 11 clusters in country)
-------------------------------------------------------------------------------------------------------
                                      |               Robust
                          vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------------------------+----------------------------------------------------------------
exploratory                           |  (base outcome)
--------------------------------------+----------------------------------------------------------------
confirmatory                          |
                       culture2#rand2 |
 Qualitative#not extensively studied  |  -2.839378    1.21571    -2.34   0.020    -5.222126   -.4566291
    Quantitative#extensively studied  |   1.432787   .5681335     2.52   0.012     .3192661    2.546308
Quantitative#not extensively studied  |  -.5783869    .606577    -0.95   0.340    -1.767256    .6104823
    Mixed-method#extensively studied  |   1.227623   .6677174     1.84   0.066    -.0810789    2.536325
Mixed-method#not extensively studied  |    -1.2036    .576943    -2.09   0.037    -2.334387   -.0728123
                                      |
                               gender |
                                Male  |   .3665223   .1789449     2.05   0.041     .0157967    .7172479
                Prefer not to answer  |   .1666586   .5157166     0.32   0.747    -.8441274    1.177445
                                      |
                            statuscat |
                      Senior Scholar  |   .2003526   .5851661     0.34   0.732    -.9465518    1.347257
                      Junior Scholar  |  -.2440452   .8304683    -0.29   0.769    -1.871733    1.383643
                                      |
                                  age |  -.0092697   .0242398    -0.38   0.702    -.0567788    .0382393
                                      |
                            continent |
                                  US  |  -.1854459   .1718348    -1.08   0.280    -.5222359    .1513442
                                _cons |    -.39575   1.793419    -0.22   0.825    -3.910786    3.119286
--------------------------------------+----------------------------------------------------------------
process                               |
                       culture2#rand2 |
 Qualitative#not extensively studied  |  -2.309943   .3926304    -5.88   0.000    -3.079485   -1.540402
    Quantitative#extensively studied  |  -.3294127   .5001809    -0.66   0.510    -1.309749    .6509239
Quantitative#not extensively studied  |   -3.10705    .475621    -6.53   0.000     -4.03925    -2.17485
    Mixed-method#extensively studied  |   .1389278    .474407     0.29   0.770    -.7908928    1.068748
Mixed-method#not extensively studied  |  -2.563082   .4762849    -5.38   0.000    -3.496583   -1.629581
                                      |
                               gender |
                                Male  |    .079302    .132143     0.60   0.548    -.1796936    .3382975
                Prefer not to answer  |   .1593184    .570028     0.28   0.780     -.957916    1.276553
                                      |
                            statuscat |
                      Senior Scholar  |    .506112   .5799479     0.87   0.383    -.6305649    1.642789
                      Junior Scholar  |    .528801   .6757877     0.78   0.434    -.7957185     1.85332
                                      |
                                  age |   .0176965   .0212586     0.83   0.405    -.0239695    .0593625
                                      |
                            continent |
                                  US  |  -.3528767   .1498959    -2.35   0.019    -.6466672   -.0590862
                                _cons |  -.0852636   1.625893    -0.05   0.958    -3.271955    3.101427
-------------------------------------------------------------------------------------------------------

. est store model

. 
. margins if rand2 == 1, at(culture2=(1 2 3) rand=(1)) post pr(out(1))

Predictive margins                              Number of obs     =        473
Model VCE    : Robust

Expression   : Pr(vig2outcome==exploratory), predict(out(1))

1._at        : culture2        =           1
               rand2           =           1

2._at        : culture2        =           2
               rand2           =           1

3._at        : culture2        =           3
               rand2           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2645587   .0863017     3.07   0.002     .0954105    .4337069
          2  |   .2059794   .0314587     6.55   0.000     .1443214    .2676373
          3  |   .1850736   .0259281     7.14   0.000     .1342555    .2358918
------------------------------------------------------------------------------

. est store ext

. est restore model
(results model are active now)

. margins if rand2 == 2, at(culture2=(1 2 3) rand2=(2)) post pr(out(1))

Predictive margins                              Number of obs     =        456
Model VCE    : Robust

Expression   : Pr(vig2outcome==exploratory), predict(out(1))

1._at        : culture2        =           1
               rand2           =           2

2._at        : culture2        =           2
               rand2           =           2

3._at        : culture2        =           3
               rand2           =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .7942177   .0193388    41.07   0.000     .7563144     .832121
          2  |   .7105587   .0376435    18.88   0.000     .6367788    .7843386
          3  |    .746149   .0297285    25.10   0.000     .6878822    .8044157
------------------------------------------------------------------------------

. est store notext

. 
. coefplot        (ext, msymbol(diamond) msize(small) col(black) ciopt(col(black))) ///
>                         (notext, msize(medium) col(gs8) ciopt(col(gs8))), ///
>         legend(off) ///
>         xlab(0(.1).9, format(%2.1f)) xtitle("Pr(Exploratory)", size(small)) ///
>         ylab(1 `" "Qualitative" "' 2 `" "Quantitative" "' ///
>                 3 `" "Mixed-Method" "', labsize(small)) grid(none)

. graph save Graph "output/figA11m1.gph", replace
(file output/figA11m1.gph saved)

. 
. est restore model
(results model are active now)

. margins if rand2 == 1, at(culture2=(1 2 3) rand=(1)) post pr(out(2))

Predictive margins                              Number of obs     =        473
Model VCE    : Robust

Expression   : Pr(vig2outcome==confirmatory), predict(out(2))

1._at        : culture2        =           1
               rand2           =           1

2._at        : culture2        =           2
               rand2           =           1

3._at        : culture2        =           3
               rand2           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1436902   .0540047     2.66   0.008     .0378429    .2495376
          2  |   .4612609   .0349883    13.18   0.000     .3926852    .5298367
          3  |   .3392231   .0265571    12.77   0.000     .2871721     .391274
------------------------------------------------------------------------------

. est store ext

. est restore model
(results model are active now)

. margins if rand2 == 2, at(culture2=(1 2 3) rand2=(2)) post pr(out(2))

Predictive margins                              Number of obs     =        456
Model VCE    : Robust

Expression   : Pr(vig2outcome==confirmatory), predict(out(2))

1._at        : culture2        =           1
               rand2           =           2

2._at        : culture2        =           2
               rand2           =           2

3._at        : culture2        =           3
               rand2           =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0255298   .0178869     1.43   0.153    -.0095279    .0605875
          2  |   .2167764   .0342054     6.34   0.000      .149735    .2838178
          3  |   .1224347   .0113238    10.81   0.000     .1002406    .1446289
------------------------------------------------------------------------------

. est store notext

. 
. coefplot        (ext, msymbol(diamond) msize(small) col(black) ciopt(col(black))) ///
>                         (notext, msize(medium) col(gs8) ciopt(col(gs8))), ///
>         legend(off) ///
>         xlab(0(.1).9, format(%2.1f)) xtitle("Pr(Confirmatory)", size(small)) ///
>         ylab(1 `" "Qualitative" "' 2 `" "Quantitative" "' ///
>                 3 `" "Mixed-Method" "', labsize(small)) grid(none)

. graph save Graph "output/figA11m2.gph", replace
(file output/figA11m2.gph saved)

. 
. est restore model
(results model are active now)

. margins if rand2 == 1, at(culture2=(1 2 3) rand=(1)) post pr(out(3))

Predictive margins                              Number of obs     =        473
Model VCE    : Robust

Expression   : Pr(vig2outcome==process), predict(out(3))

1._at        : culture2        =           1
               rand2           =           1

2._at        : culture2        =           2
               rand2           =           1

3._at        : culture2        =           3
               rand2           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .591751   .0898447     6.59   0.000     .4156587    .7678433
          2  |   .3327597   .0291882    11.40   0.000     .2755519    .3899674
          3  |   .4757033   .0342908    13.87   0.000     .4084946     .542912
------------------------------------------------------------------------------

. est store ext

. est restore model
(results model are active now)

. margins if rand2 == 2, at(culture2=(1 2 3) rand2=(2)) post pr(out(3))

Predictive margins                              Number of obs     =        456
Model VCE    : Robust

Expression   : Pr(vig2outcome==process), predict(out(3))

1._at        : culture2        =           1
               rand2           =           2

2._at        : culture2        =           2
               rand2           =           2

3._at        : culture2        =           3
               rand2           =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1802525   .0264498     6.81   0.000     .1284118    .2320931
          2  |   .0726649   .0192143     3.78   0.000     .0350056    .1103242
          3  |   .1314163   .0290983     4.52   0.000     .0743846     .188448
------------------------------------------------------------------------------

. est store notext

. 
. coefplot        (ext, msymbol(diamond) msize(small) col(black) ciopt(col(black))) ///
>                         (notext, msize(medium) col(gs8) ciopt(col(gs8))), ///
>         legend(order(2 "extensively studied" 4 "not extensively studied") position(6) cols(2)) ///
>         xlab(0(.1).9, format(%2.1f)) xtitle("Pr(Process)", size(small)) ///
>         ylab(1 `" "Qualitative" "' 2 `" "Quantitative" "' ///
>                 3 `" "Mixed-Method" "', labsize(small)) grid(none)

. graph save Graph "output/figA11m3.gph", replace
(file output/figA11m3.gph saved)

. 
. graph combine "output/figA11m1.gph" "output/figA11m2.gph" "output/figA11m3.gph", rows(3) imargin(small) title("") commonscheme graphregion(color(white) margin(small) fcolor(white)) xcommon ycommon

. graph save Graph "output/figA11.gph", replace
(file output/figA11.gph saved)

. graph export "output/figureA11.tif", width(1000) replace
(file output/figureA11.tif written in TIFF format)

. est clear

. 
. *Figure A12: Treatment effect accross continents with alternative coding of research culture
. mlogit vig2outcome i.culture2 i.continent#i.rand2 i.gender i.statuscat age, robust cluster(country)

Iteration 0:   log pseudolikelihood = -983.65333  
Iteration 1:   log pseudolikelihood = -805.18742  
Iteration 2:   log pseudolikelihood = -803.05885  
Iteration 3:   log pseudolikelihood = -803.05426  
Iteration 4:   log pseudolikelihood = -803.05426  

Multinomial logistic regression                 Number of obs     =        929
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -803.05426               Pseudo R2         =     0.1836

                                              (Std. Err. adjusted for 11 clusters in country)
---------------------------------------------------------------------------------------------
                            |               Robust
                vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
exploratory                 |  (base outcome)
----------------------------+----------------------------------------------------------------
confirmatory                |
                   culture2 |
              Quantitative  |   1.619424   .3557753     4.55   0.000     .9221175    2.316731
              Mixed-method  |   1.224749   .3139663     3.90   0.000     .6093862    1.840112
                            |
            continent#rand2 |
EU#not extensively studied  |  -2.468103   .3799822    -6.50   0.000    -3.212855   -1.723352
    US#extensively studied  |  -.4851136   .3179511    -1.53   0.127    -1.108286    .1380591
US#not extensively studied  |  -2.585369   .3207866    -8.06   0.000      -3.2141   -1.956639
                            |
                     gender |
                      Male  |   .3622826   .1765609     2.05   0.040     .0162295    .7083357
      Prefer not to answer  |    .122434   .5291252     0.23   0.817    -.9146323      1.1595
                            |
                  statuscat |
            Senior Scholar  |   .2218294   .5910028     0.38   0.707    -.9365148    1.380174
            Junior Scholar  |  -.2476789   .8465087    -0.29   0.770    -1.906806    1.411448
                            |
                        age |  -.0094206   .0245406    -0.38   0.701    -.0575194    .0386782
                      _cons |  -.2943377   1.679596    -0.18   0.861    -3.586284    2.997609
----------------------------+----------------------------------------------------------------
process                     |
                   culture2 |
              Quantitative  |  -.4287398   .2887303    -1.48   0.138    -.9946409    .1371613
              Mixed-method  |  -.0395066   .2662209    -0.15   0.882      -.56129    .4822767
                            |
            continent#rand2 |
EU#not extensively studied  |  -3.167528   .3681532    -8.60   0.000    -3.889095   -2.445961
    US#extensively studied  |  -.7965113   .3189081    -2.50   0.013     -1.42156    -.171463
US#not extensively studied  |   -2.90645   .3300848    -8.81   0.000    -3.553404   -2.259495
                            |
                     gender |
                      Male  |   .0757027   .1337093     0.57   0.571    -.1863627    .3377682
      Prefer not to answer  |    .146219   .6032288     0.24   0.808    -1.036088    1.328526
                            |
                  statuscat |
            Senior Scholar  |    .583722   .6142257     0.95   0.342    -.6201383    1.787582
            Junior Scholar  |   .5950002   .7329098     0.81   0.417    -.8414765    2.031477
                            |
                        age |   .0200414   .0236974     0.85   0.398    -.0264047    .0664875
                      _cons |   .1453908   1.744065     0.08   0.934    -3.272914    3.563695
---------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2) at(continent=(1 2)) pr(out(1)) post

Average marginal effects                        Number of obs     =        929
Model VCE    : Robust

Expression   : Pr(vig2outcome==exploratory), predict(out(1))
dy/dx w.r.t. : 2.rand2

1._at        : continent       =           1

2._at        : continent       =           2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.rand2      |  (base outcome)
-------------+----------------------------------------------------------------
2.rand2      |
         _at |
          1  |   .5946471   .0484977    12.26   0.000     .4995935    .6897008
          2  |   .4782305   .0069551    68.76   0.000     .4645988    .4918622
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, scheme(plottig) xline(0, lpattern(dash) lcolor(black)) title(Exploratory, size(small)) coeflabels(1._at = "EU" 2._at = "US") xscale(range(-0.7 0.7)) xlabel(-0.7(0.1)0.7) nodraw mcolor(blac
> k) mfcolor(black) msymbol(diamond)  ciopts(lcolor(black))
(note:  alignstroke foreground not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke tick not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke grid not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke major_grid not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke minortick not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke axisline not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke background not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke plotregion not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1mark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1 not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1other not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke dotmark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  anglestyle symbol not found in scheme, default attributes used)
(note:  anglestyle symbol not found in scheme, default attributes used)
(note:  alignstroke p2mark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p2 not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p2other not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke xyline not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)

. graph save Graph "output/figA12m1.gph", replace
(file output/figA12m1.gph saved)

. est clear

. mlogit vig2outcome i.culture2 i.continent#i.rand2 i.gender i.statuscat age, robust cluster(country)

Iteration 0:   log pseudolikelihood = -983.65333  
Iteration 1:   log pseudolikelihood = -805.18742  
Iteration 2:   log pseudolikelihood = -803.05885  
Iteration 3:   log pseudolikelihood = -803.05426  
Iteration 4:   log pseudolikelihood = -803.05426  

Multinomial logistic regression                 Number of obs     =        929
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -803.05426               Pseudo R2         =     0.1836

                                              (Std. Err. adjusted for 11 clusters in country)
---------------------------------------------------------------------------------------------
                            |               Robust
                vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
exploratory                 |  (base outcome)
----------------------------+----------------------------------------------------------------
confirmatory                |
                   culture2 |
              Quantitative  |   1.619424   .3557753     4.55   0.000     .9221175    2.316731
              Mixed-method  |   1.224749   .3139663     3.90   0.000     .6093862    1.840112
                            |
            continent#rand2 |
EU#not extensively studied  |  -2.468103   .3799822    -6.50   0.000    -3.212855   -1.723352
    US#extensively studied  |  -.4851136   .3179511    -1.53   0.127    -1.108286    .1380591
US#not extensively studied  |  -2.585369   .3207866    -8.06   0.000      -3.2141   -1.956639
                            |
                     gender |
                      Male  |   .3622826   .1765609     2.05   0.040     .0162295    .7083357
      Prefer not to answer  |    .122434   .5291252     0.23   0.817    -.9146323      1.1595
                            |
                  statuscat |
            Senior Scholar  |   .2218294   .5910028     0.38   0.707    -.9365148    1.380174
            Junior Scholar  |  -.2476789   .8465087    -0.29   0.770    -1.906806    1.411448
                            |
                        age |  -.0094206   .0245406    -0.38   0.701    -.0575194    .0386782
                      _cons |  -.2943377   1.679596    -0.18   0.861    -3.586284    2.997609
----------------------------+----------------------------------------------------------------
process                     |
                   culture2 |
              Quantitative  |  -.4287398   .2887303    -1.48   0.138    -.9946409    .1371613
              Mixed-method  |  -.0395066   .2662209    -0.15   0.882      -.56129    .4822767
                            |
            continent#rand2 |
EU#not extensively studied  |  -3.167528   .3681532    -8.60   0.000    -3.889095   -2.445961
    US#extensively studied  |  -.7965113   .3189081    -2.50   0.013     -1.42156    -.171463
US#not extensively studied  |   -2.90645   .3300848    -8.81   0.000    -3.553404   -2.259495
                            |
                     gender |
                      Male  |   .0757027   .1337093     0.57   0.571    -.1863627    .3377682
      Prefer not to answer  |    .146219   .6032288     0.24   0.808    -1.036088    1.328526
                            |
                  statuscat |
            Senior Scholar  |    .583722   .6142257     0.95   0.342    -.6201383    1.787582
            Junior Scholar  |   .5950002   .7329098     0.81   0.417    -.8414765    2.031477
                            |
                        age |   .0200414   .0236974     0.85   0.398    -.0264047    .0664875
                      _cons |   .1453908   1.744065     0.08   0.934    -3.272914    3.563695
---------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2) at(continent=(1 2)) pr(out(2)) post

Average marginal effects                        Number of obs     =        929
Model VCE    : Robust

Expression   : Pr(vig2outcome==confirmatory), predict(out(2))
dy/dx w.r.t. : 2.rand2

1._at        : continent       =           1

2._at        : continent       =           2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.rand2      |  (base outcome)
-------------+----------------------------------------------------------------
2.rand2      |
         _at |
          1  |  -.1948462   .0408691    -4.77   0.000    -.2749481   -.1147443
          2  |  -.2238653   .0082995   -26.97   0.000    -.2401321   -.2075985
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, scheme(plottig) xline(0, lpattern(dash) lcolor(black)) title(Confirmatory, size(small)) coeflabels(1._at = "EU" 2._at = "US") xscale(range(-0.7 0.7)) xlabel(-0.7(0.1)0.7) nodraw mcolor(bla
> ck) mfcolor(black) msymbol(diamond)  ciopts(lcolor(black))
(note:  alignstroke foreground not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke tick not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke grid not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke major_grid not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke minortick not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke axisline not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke background not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke plotregion not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1mark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1 not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1other not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke dotmark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  anglestyle symbol not found in scheme, default attributes used)
(note:  anglestyle symbol not found in scheme, default attributes used)
(note:  alignstroke p2mark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p2 not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p2other not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke xyline not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)

. graph save Graph "output/figA12m2.gph", replace
(file output/figA12m2.gph saved)

. est clear

. mlogit vig2outcome i.culture2 i.continent#i.rand2 i.gender i.statuscat age, robust cluster(country)

Iteration 0:   log pseudolikelihood = -983.65333  
Iteration 1:   log pseudolikelihood = -805.18742  
Iteration 2:   log pseudolikelihood = -803.05885  
Iteration 3:   log pseudolikelihood = -803.05426  
Iteration 4:   log pseudolikelihood = -803.05426  

Multinomial logistic regression                 Number of obs     =        929
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -803.05426               Pseudo R2         =     0.1836

                                              (Std. Err. adjusted for 11 clusters in country)
---------------------------------------------------------------------------------------------
                            |               Robust
                vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
exploratory                 |  (base outcome)
----------------------------+----------------------------------------------------------------
confirmatory                |
                   culture2 |
              Quantitative  |   1.619424   .3557753     4.55   0.000     .9221175    2.316731
              Mixed-method  |   1.224749   .3139663     3.90   0.000     .6093862    1.840112
                            |
            continent#rand2 |
EU#not extensively studied  |  -2.468103   .3799822    -6.50   0.000    -3.212855   -1.723352
    US#extensively studied  |  -.4851136   .3179511    -1.53   0.127    -1.108286    .1380591
US#not extensively studied  |  -2.585369   .3207866    -8.06   0.000      -3.2141   -1.956639
                            |
                     gender |
                      Male  |   .3622826   .1765609     2.05   0.040     .0162295    .7083357
      Prefer not to answer  |    .122434   .5291252     0.23   0.817    -.9146323      1.1595
                            |
                  statuscat |
            Senior Scholar  |   .2218294   .5910028     0.38   0.707    -.9365148    1.380174
            Junior Scholar  |  -.2476789   .8465087    -0.29   0.770    -1.906806    1.411448
                            |
                        age |  -.0094206   .0245406    -0.38   0.701    -.0575194    .0386782
                      _cons |  -.2943377   1.679596    -0.18   0.861    -3.586284    2.997609
----------------------------+----------------------------------------------------------------
process                     |
                   culture2 |
              Quantitative  |  -.4287398   .2887303    -1.48   0.138    -.9946409    .1371613
              Mixed-method  |  -.0395066   .2662209    -0.15   0.882      -.56129    .4822767
                            |
            continent#rand2 |
EU#not extensively studied  |  -3.167528   .3681532    -8.60   0.000    -3.889095   -2.445961
    US#extensively studied  |  -.7965113   .3189081    -2.50   0.013     -1.42156    -.171463
US#not extensively studied  |   -2.90645   .3300848    -8.81   0.000    -3.553404   -2.259495
                            |
                     gender |
                      Male  |   .0757027   .1337093     0.57   0.571    -.1863627    .3377682
      Prefer not to answer  |    .146219   .6032288     0.24   0.808    -1.036088    1.328526
                            |
                  statuscat |
            Senior Scholar  |    .583722   .6142257     0.95   0.342    -.6201383    1.787582
            Junior Scholar  |   .5950002   .7329098     0.81   0.417    -.8414765    2.031477
                            |
                        age |   .0200414   .0236974     0.85   0.398    -.0264047    .0664875
                      _cons |   .1453908   1.744065     0.08   0.934    -3.272914    3.563695
---------------------------------------------------------------------------------------------

. eststo m

. est res m
(results m are active now)

. margins, dydx(rand2) at(continent=(1 2)) pr(out(3)) post

Average marginal effects                        Number of obs     =        929
Model VCE    : Robust

Expression   : Pr(vig2outcome==process), predict(out(3))
dy/dx w.r.t. : 2.rand2

1._at        : continent       =           1

2._at        : continent       =           2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.rand2      |  (base outcome)
-------------+----------------------------------------------------------------
2.rand2      |
         _at |
          1  |  -.3998009   .0449244    -8.90   0.000    -.4878511   -.3117508
          2  |  -.2543652   .0066463   -38.27   0.000    -.2673918   -.2413387
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. coefplot, scheme(plottig) xline(0, lpattern(dash) lcolor(black)) title(Process, size(small)) coeflabels(1._at = "EU" 2._at = "US") xscale(range(-0.7 0.7)) xlabel(-0.7(0.1)0.7) nodraw mcolor(black) m
> fcolor(black) msymbol(diamond)  ciopts(lcolor(black))
(note:  alignstroke foreground not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke tick not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke grid not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke major_grid not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke minortick not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke axisline not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke background not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke plotregion not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1mark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1 not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p1other not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke dotmark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  anglestyle symbol not found in scheme, default attributes used)
(note:  anglestyle symbol not found in scheme, default attributes used)
(note:  alignstroke p2mark not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p2 not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke p2other not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)
(note:  alignstroke xyline not found in scheme, default attributes used)
(note:  alignstroke not found in scheme, default attributes used)

. graph save Graph "output/figA12m3.gph", replace
(file output/figA12m3.gph saved)

. est clear

. graph combine "output/figA12m1.gph" "output/figA12m2.gph" "output/figA12m3.gph", rows(3) imargin(small) title("") commonscheme graphregion(color(white) margin(small) fcolor(white)) ycommon

. graph save Graph "output/figA12.gph", replace
(file output/figA12.gph saved)

. graph export "output/figureA12.tif", width(1000) replace
(file output/figureA12.tif written in TIFF format)

. est clear

. 
. *Figure A13: Probability of choosing a specific goal of inference across continents and treatment conditions with alternative coding of research culture
. mlogit vig2outcome i.culture2 i.continent#i.rand2 i.gender i.statuscat age, robust cluster(country)

Iteration 0:   log pseudolikelihood = -983.65333  
Iteration 1:   log pseudolikelihood = -805.18742  
Iteration 2:   log pseudolikelihood = -803.05885  
Iteration 3:   log pseudolikelihood = -803.05426  
Iteration 4:   log pseudolikelihood = -803.05426  

Multinomial logistic regression                 Number of obs     =        929
                                                Wald chi2(8)      =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -803.05426               Pseudo R2         =     0.1836

                                              (Std. Err. adjusted for 11 clusters in country)
---------------------------------------------------------------------------------------------
                            |               Robust
                vig2outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
exploratory                 |  (base outcome)
----------------------------+----------------------------------------------------------------
confirmatory                |
                   culture2 |
              Quantitative  |   1.619424   .3557753     4.55   0.000     .9221175    2.316731
              Mixed-method  |   1.224749   .3139663     3.90   0.000     .6093862    1.840112
                            |
            continent#rand2 |
EU#not extensively studied  |  -2.468103   .3799822    -6.50   0.000    -3.212855   -1.723352
    US#extensively studied  |  -.4851136   .3179511    -1.53   0.127    -1.108286    .1380591
US#not extensively studied  |  -2.585369   .3207866    -8.06   0.000      -3.2141   -1.956639
                            |
                     gender |
                      Male  |   .3622826   .1765609     2.05   0.040     .0162295    .7083357
      Prefer not to answer  |    .122434   .5291252     0.23   0.817    -.9146323      1.1595
                            |
                  statuscat |
            Senior Scholar  |   .2218294   .5910028     0.38   0.707    -.9365148    1.380174
            Junior Scholar  |  -.2476789   .8465087    -0.29   0.770    -1.906806    1.411448
                            |
                        age |  -.0094206   .0245406    -0.38   0.701    -.0575194    .0386782
                      _cons |  -.2943377   1.679596    -0.18   0.861    -3.586284    2.997609
----------------------------+----------------------------------------------------------------
process                     |
                   culture2 |
              Quantitative  |  -.4287398   .2887303    -1.48   0.138    -.9946409    .1371613
              Mixed-method  |  -.0395066   .2662209    -0.15   0.882      -.56129    .4822767
                            |
            continent#rand2 |
EU#not extensively studied  |  -3.167528   .3681532    -8.60   0.000    -3.889095   -2.445961
    US#extensively studied  |  -.7965113   .3189081    -2.50   0.013     -1.42156    -.171463
US#not extensively studied  |   -2.90645   .3300848    -8.81   0.000    -3.553404   -2.259495
                            |
                     gender |
                      Male  |   .0757027   .1337093     0.57   0.571    -.1863627    .3377682
      Prefer not to answer  |    .146219   .6032288     0.24   0.808    -1.036088    1.328526
                            |
                  statuscat |
            Senior Scholar  |    .583722   .6142257     0.95   0.342    -.6201383    1.787582
            Junior Scholar  |   .5950002   .7329098     0.81   0.417    -.8414765    2.031477
                            |
                        age |   .0200414   .0236974     0.85   0.398    -.0264047    .0664875
                      _cons |   .1453908   1.744065     0.08   0.934    -3.272914    3.563695
---------------------------------------------------------------------------------------------

. est store model

. 
. margins if rand2 == 1, at(continent=(1 2)) post pr(out(1))

Predictive margins                              Number of obs     =        473
Model VCE    : Robust

Expression   : Pr(vig2outcome==exploratory), predict(out(1))

1._at        : continent       =           1

2._at        : continent       =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .149456   .0422085     3.54   0.000     .0667288    .2321832
          2  |   .2533386   .0077316    32.77   0.000      .238185    .2684923
------------------------------------------------------------------------------

. est store       ext

. est restore model
(results model are active now)

. margins if rand2 == 2, at(continent=(1 2)) post pr(out(1))

Predictive margins                              Number of obs     =        456
Model VCE    : Robust

Expression   : Pr(vig2outcome==exploratory), predict(out(1))

1._at        : continent       =           1

2._at        : continent       =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .7469506   .0216496    34.50   0.000     .7045182    .7893831
          2  |   .7347654    .004712   155.94   0.000     .7255301    .7440007
------------------------------------------------------------------------------

. est store       notext

. 
. coefplot        (ext, msymbol(diamond) msize(small) col(black) ciopt(col(black))) ///
>                         (notext, msize(medium) col(gs8) ciopt(col(gs8))), ///
>         legend(off) ///
>         xlab(0(.1).9, format(%2.1f)) xtitle("Pr(Exploratory)", size(small)) ///
>         ylab(1 `" "EU" "' 2 `" "US" "', labsize(small)) grid(none)

. graph save Graph "output/figA13m1.gph", replace
(file output/figA13m1.gph saved)

. 
. est restore model
(results model are active now)

. margins if rand2 == 1, at(continent=(1 2)) post pr(out(2))

Predictive margins                              Number of obs     =        473
Model VCE    : Robust

Expression   : Pr(vig2outcome==confirmatory), predict(out(2))

1._at        : continent       =           1

2._at        : continent       =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .3469522   .0290813    11.93   0.000     .2899539    .4039505
          2  |   .3586351   .0082607    43.41   0.000     .3424444    .3748258
------------------------------------------------------------------------------

. est store       ext

. est restore model
(results model are active now)

. margins if rand2 == 2, at(continent=(1 2)) post pr(out(2))

Predictive margins                              Number of obs     =        456
Model VCE    : Robust

Expression   : Pr(vig2outcome==confirmatory), predict(out(2))

1._at        : continent       =           1

2._at        : continent       =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1449167   .0257713     5.62   0.000     .0944059    .1954276
          2  |   .1275796   .0032943    38.73   0.000     .1211229    .1340364
------------------------------------------------------------------------------

. est store       notext

. coefplot        (ext, msymbol(diamond) msize(small) col(black) ciopt(col(black))) ///
>                         (notext, msize(medium) col(gs8) ciopt(col(gs8))), ///
>         legend(off) ///
>         xlab(0(.1).9, format(%2.1f)) xtitle("Pr(Confirmatory)", size(small)) ///
>         ylab(1 `" "EU" "' 2 `" "US" "', labsize(small)) grid(none)

. graph save Graph "output/figA13m2.gph", replace
(file output/figA13m2.gph saved)

.         
. est restore model
(results model are active now)

. margins if rand2 == 1, at(continent=(1 2)) post pr(out(3))

Predictive margins                              Number of obs     =        473
Model VCE    : Robust

Expression   : Pr(vig2outcome==process), predict(out(3))

1._at        : continent       =           1

2._at        : continent       =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .5035918   .0423085    11.90   0.000     .4206687     .586515
          2  |   .3880263   .0085645    45.31   0.000     .3712402    .4048124
------------------------------------------------------------------------------

. est store       ext

. est restore model
(results model are active now)

. margins if rand2 == 2, at(continent=(1 2)) post pr(out(3))

Predictive margins                              Number of obs     =        456
Model VCE    : Robust

Expression   : Pr(vig2outcome==process), predict(out(3))

1._at        : continent       =           1

2._at        : continent       =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1081326   .0116777     9.26   0.000     .0852448    .1310204
          2  |    .137655   .0041166    33.44   0.000     .1295865    .1457234
------------------------------------------------------------------------------

. est store       notext

. coefplot        (ext, msymbol(diamond) msize(small) col(black) ciopt(col(black))) ///
>                         (notext, msize(medium) col(gs8) ciopt(col(gs8))), ///
>         legend(order(2 "extensively studied" 4 "not extensively studied") position(6) cols(2)) ///
>         xlab(0(.1).9, format(%2.1f)) xtitle("Pr(Process)", size(small)) ///
>         ylab(1 `" "EU" "' 2 `" "US" "', labsize(small)) grid(none)

. graph save Graph "output/figA13m3.gph", replace
(file output/figA13m3.gph saved)

. 
. graph combine "output/figA13m1.gph" "output/figA13m2.gph" "output/figA13m3.gph", rows(3) imargin(small) title("") commonscheme graphregion(color(white) margin(small) fcolor(white)) xcommon ycommon

. graph save Graph "output/figA13.gph", replace
(file output/figA13.gph saved)

. graph export "output/figureA13.tif", width(1000) replace
(file output/figureA13.tif written in TIFF format)

. est clear

. 
. ***Table A16: Distribution of method preferences in the US and EU
. asdoc tab culture continent, col title(Table A16: Distribution of method preferences in the US and EU) replace  save(output/tableA16.doc)

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

               |       continent
       culture |        EU         US |     Total
---------------+----------------------+----------
   Qualitative |       114         36 |       150 
               |     26.21       7.73 |     16.65 
---------------+----------------------+----------
  Quantitative |       134        175 |       309 
               |     30.80      37.55 |     34.30 
---------------+----------------------+----------
  Mixed-method |       187        255 |       442 
               |     42.99      54.72 |     49.06 
---------------+----------------------+----------
         Total |       435        466 |       901 
               |    100.00     100.00 |    100.00 
Click to Open File:  output/tableA16.doc

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